Rank-normalization, folding, and localization: An improved \(\widehat{R}\) for assessing convergence of MCMC (online appendix)
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian Bürkner
Major version 2019-03-19. Minor modifications 2019-07-05.
1 Overview
This is the online appendix of the paper
- Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian Bürkner (2019): Rank-normalization, folding, and localization: An improved \(\widehat{R}\) for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
The code for the paper is available on Github (https://github.com/avehtari/rhat_ess). Here, we introduce all the code related to the examples presented in the paper and more numerical experiments not discussed in the paper itself.
To help you finding your way through all the examples presented in this online appendix, below please find a list of links to the examples discussed in the paper:
- Cauchy: A distribution with infinite mean and variance
- Hierarchical model: Eight schools
- Appendix A: Computing the effective sample size
- Appendix B: Normal distributions with additional trend, shift or scaling
- Appendix C: More experiments with the Cauchy distribution
- Appendix D: A centered eight schools model with very long chains and thinning
- Appendix E: A centered eight schools model fit using a Gibbs sampler
2 Examples
In this section, we will go through some examples to demonstrate the usefulness of our proposed methods as well as the associated workflow in determining convergence. Appendices B-E contain more detailed analysis of different algorithm variants and further examples.
First, we load all the necessary R packages and additional functions.
library(tidyverse)
library(gridExtra)
library(rstan)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
library(bayesplot)
theme_set(bayesplot::theme_default(base_family = "sans"))
library(rjags)
library(abind)
source('monitornew.R')
source('monitorplot.R')2.1 Cauchy: A distribution with infinite mean and variance
This section relates to the examples presented in Section 5.1 of the paper.
Traditional \(\widehat{R}\) is based on calculating within and between chain variances. If the marginal distribution of a chain is such that the variance is infinite, this approach is not well justified, as we demonstrate here with a Cauchy-distributed example. The following Cauchy models are from Michael Betancourt’s case study Fitting The Cauchy Distribution. Appendix C contains more detailed analysis of different algorithm variants and further Cauchy examples.
2.1.1 Nominal parameterization of Cauchy
The nominal Cauchy model with direct parameterization is as follows:
writeLines(readLines("cauchy_nom.stan"))parameters {
vector[50] x;
}
model {
x ~ cauchy(0, 1);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}2.1.1.1 Default Stan options
Run the nominal model:
fit_nom <- stan(file = 'cauchy_nom.stan', seed = 7878, refresh = 0)Warning: There were 1421 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceededWarning: Examine the pairs() plot to diagnose sampling problemsWe get HMC specific diagnostic (Betancourt, 2017) warnings about a very large number of transitions after warmup that exceed the maximum treedepth and low estimated Bayesian fraction of missing information, indicating slow mixing likely due to very long tails of the Cauchy distribution.
mon <- monitor(fit_nom)
print(mon)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x[1] -5.87 0.02 6.78 0.92 13.64 1.01 1967 360
x[2] -5.76 -0.03 5.11 -0.27 7.53 1.00 2417 802
x[3] -6.12 0.00 7.73 0.77 10.28 1.01 448 82
x[4] -5.30 -0.01 5.41 -0.06 7.19 1.01 2266 697
x[5] -13.14 -0.04 5.83 -2.78 17.58 1.03 169 45
x[6] -8.10 -0.06 6.39 -0.92 15.59 1.01 1611 402
x[7] -7.25 -0.08 7.55 -0.29 9.68 1.01 1227 462
x[8] -4.79 0.02 6.23 0.57 8.65 1.02 1568 432
x[9] -4.69 0.01 4.82 0.13 5.43 1.00 2607 784
x[10] -8.43 -0.04 6.50 -1.92 32.96 1.01 1609 261
x[11] -6.21 0.02 7.09 0.43 16.21 1.01 2464 474
x[12] -5.55 0.02 6.62 0.17 6.37 1.00 2813 404
x[13] -6.16 0.00 6.13 0.44 12.78 1.01 2282 565
x[14] -5.45 0.02 6.79 0.29 8.44 1.01 3121 647
x[15] -8.17 0.05 9.55 0.26 30.61 1.01 2126 521
x[16] -5.83 0.00 6.23 0.31 8.60 1.01 2177 589
x[17] -18.10 -0.07 7.39 -6.89 47.38 1.03 209 67
x[18] -5.89 0.05 5.90 0.10 5.42 1.01 3336 664
x[19] -7.04 0.03 6.23 -0.23 12.26 1.01 1434 421
x[20] -6.58 0.03 7.53 -0.19 7.81 1.02 952 446
x[21] -8.11 0.01 6.57 -2.16 28.47 1.00 1790 397
x[22] -5.47 0.04 5.24 0.25 8.30 1.00 2841 712
x[23] -10.04 -0.05 6.55 -11.16 87.97 1.04 318 47
x[24] -13.18 -0.07 5.72 -4.58 30.89 1.01 441 98
x[25] -6.89 -0.02 5.60 -0.44 8.76 1.02 1892 490
x[26] -5.33 -0.02 4.69 -0.30 6.55 1.01 3099 732
x[27] -5.19 -0.04 5.17 -0.26 8.50 1.00 3096 874
x[28] -6.81 -0.02 6.81 0.15 7.71 1.00 4185 924
x[29] -9.96 -0.02 6.99 -1.66 18.87 1.01 935 223
x[30] -6.20 0.02 6.62 0.44 14.44 1.01 3504 559
x[31] -18.84 -0.04 5.77 -4.22 29.48 1.02 210 49
x[32] -6.33 -0.01 6.21 -0.02 6.73 1.02 3039 748
x[33] -4.81 0.03 4.93 0.22 7.89 1.01 3505 1008
x[34] -5.75 0.02 5.75 -0.12 9.23 1.00 1896 661
x[35] -5.98 0.03 7.16 0.06 8.68 1.01 2706 639
x[36] -7.37 0.00 9.19 0.08 10.82 1.01 1405 399
x[37] -5.03 0.02 7.82 2.52 29.21 1.01 2029 357
x[38] -5.40 -0.01 5.59 -0.06 6.64 1.01 3611 741
x[39] -4.78 -0.02 5.36 0.27 6.40 1.00 2855 757
x[40] -6.16 0.00 4.58 -0.91 10.45 1.01 1860 457
x[41] -5.46 0.07 6.55 0.05 6.06 1.01 2328 620
x[42] -7.63 -0.05 6.38 -0.75 10.10 1.01 1796 513
x[43] -7.47 -0.04 6.39 -0.59 8.32 1.01 1876 558
x[44] -5.22 0.00 5.17 0.11 6.15 1.01 1610 575
x[45] -4.44 0.04 13.98 3.19 17.67 1.01 290 64
x[46] -5.04 0.01 5.60 0.22 9.06 1.02 3154 398
x[47] -5.87 0.03 5.46 0.19 11.73 1.02 2258 497
x[48] -4.99 -0.03 4.91 0.05 10.22 1.01 2916 724
x[49] -6.42 0.01 10.78 10.69 83.66 1.02 214 43
x[50] -6.24 0.02 5.71 0.41 19.28 1.00 2615 788
I 0.00 1.00 1.00 0.51 0.50 1.00 688 688
lp__ -88.54 -68.47 -50.77 -69.10 11.75 1.01 252 453
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).which_min_ess <- which.min(mon[1:50, 'Tail_ESS'])Several values of Rhat greater than 1.01 and some ESS less than 400 also indicate convergence propblems. The Appendix C contains more results with longer chains.
We can further analyze potential problems using local efficiency and rank plots. We specifically investigate x[49], which, in this specific run, had the smallest tail-ESS of 43.
We examine the sampling efficiency in different parts of the posterior by computing the efficiency of small interval probability estimates (see Section Efficiency estimate for small interval probability estimates). Each interval contains \(1/k\) of the draws (e.g., \(5\%\) each, if \(k=20\)). The small interval efficiency measures mixing of an function which indicates when the values are inside or outside the specific small interval. As detailed above, this gives us a local efficiency measure which does not depend on the shape of the distribution.
plot_local_ess(fit = fit_nom, par = which_min_ess, nalpha = 20)
The efficiency of sampling is low in the tails, which is clearly caused by slow mixing in long tails of the Cauchy distribution.
Orange ticks show iterations that exceeded the maximum treedepth.
An alternative way to examine the efficiency in different parts of the posterior is to compute efficiency estimates for quantiles (see Section Efficiency for quantiles). Each interval has a specified proportion of draws, and the efficiency measures mixing of a function which indicates when the values are smaller than or equal to the corresponding quantile.
plot_quantile_ess(fit = fit_nom, par = which_min_ess, nalpha = 40)
Similar as above, we see that the efficiency of sampling is low in the tails. Orange ticks show iterations that exceeded the maximum treedepth.
We may also investigate how the estimated effective sample sizes change when we use more and more draws; Brooks and Gelman (1998) proposed to use similar graph for \(\widehat{R}\). If the effective sample size is highly unstable, does not increase proportionally with more draws, or even decreases, this indicates that simply running longer chains will likely not solve the convergence issues. In the plot below, we see how unstable both bulk-ESS and tail-ESS are for this example.
plot_change_ess(fit = fit_nom, par = which_min_ess)
We can further analyze potential problems using rank plots which clearly show the mixing problem between chains. In case of good mixing all rank plots should be close to uniform.
samp <- as.array(fit_nom)
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
2.1.2 Alternative parameterization of Cauchy
Next, we examine an alternative parameterization of the Cauchy as a scale mixture of Gaussians. The model has two parameters, and the Cauchy distributed \(x\) can be computed deterministically from those. In addition to improved sampling performance, the example illustrates that focusing on diagnostics matters.
writeLines(readLines("cauchy_alt_1.stan"))parameters {
vector[50] x_a;
vector<lower=0>[50] x_b;
}
transformed parameters {
vector[50] x = x_a ./ sqrt(x_b);
}
model {
x_a ~ normal(0, 1);
x_b ~ gamma(0.5, 0.5);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}Run the alternative model:
fit_alt1 <- stan(file = 'cauchy_alt_1.stan', seed = 7878, refresh = 0)There are no warnings, and the sampling is much faster.
mon <- monitor(fit_alt1)
print(mon)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x_a[1] -1.68 -0.03 1.60 -0.02 1.00 1 4259 3068
x_a[2] -1.59 -0.01 1.71 0.02 1.00 1 3650 3149
x_a[3] -1.62 0.00 1.61 0.01 1.00 1 4090 2890
x_a[4] -1.66 -0.02 1.58 -0.02 0.99 1 4145 3085
x_a[5] -1.69 -0.01 1.72 -0.01 1.02 1 4571 2518
x_a[6] -1.67 0.01 1.71 0.00 1.02 1 4279 2994
x_a[7] -1.69 0.03 1.71 0.02 1.01 1 4070 2909
x_a[8] -1.61 0.01 1.64 0.01 0.99 1 3998 2889
x_a[9] -1.71 0.02 1.64 0.01 1.00 1 3866 2989
x_a[10] -1.63 -0.01 1.60 -0.01 1.00 1 3932 2749
x_a[11] -1.60 -0.04 1.61 -0.01 0.99 1 4217 3576
x_a[12] -1.64 0.01 1.64 0.00 1.00 1 3674 2689
x_a[13] -1.70 -0.01 1.68 -0.01 1.02 1 3887 2907
x_a[14] -1.69 0.03 1.66 0.02 1.00 1 3960 2728
x_a[15] -1.63 -0.01 1.67 0.00 1.00 1 3742 3187
x_a[16] -1.65 -0.01 1.65 0.00 1.00 1 4343 3251
x_a[17] -1.66 -0.02 1.67 0.00 1.01 1 4344 2881
x_a[18] -1.56 0.01 1.55 0.00 0.95 1 3917 2635
x_a[19] -1.61 -0.02 1.65 -0.02 0.99 1 4046 2885
x_a[20] -1.64 0.01 1.72 0.01 1.01 1 4216 2881
x_a[21] -1.62 -0.01 1.58 -0.01 0.98 1 4313 2549
x_a[22] -1.61 0.00 1.61 -0.01 0.98 1 3873 3011
x_a[23] -1.69 -0.02 1.62 -0.03 1.00 1 3858 3068
x_a[24] -1.61 -0.02 1.61 -0.01 0.98 1 3459 3014
x_a[25] -1.65 0.01 1.64 0.00 1.01 1 4311 2984
x_a[26] -1.60 0.05 1.64 0.03 0.98 1 3960 3115
x_a[27] -1.64 -0.02 1.66 -0.02 1.01 1 3585 2799
x_a[28] -1.62 0.01 1.59 0.01 0.99 1 3899 2891
x_a[29] -1.60 0.02 1.58 0.02 0.97 1 4386 3173
x_a[30] -1.65 0.02 1.70 0.02 1.01 1 4215 3112
x_a[31] -1.64 0.00 1.64 0.00 0.98 1 3864 2662
x_a[32] -1.67 -0.01 1.65 0.00 1.00 1 4340 2693
x_a[33] -1.65 -0.02 1.59 -0.01 0.99 1 4231 2781
x_a[34] -1.67 0.02 1.69 0.01 1.00 1 4170 3166
x_a[35] -1.62 0.00 1.58 -0.01 0.98 1 3704 2632
x_a[36] -1.68 0.01 1.65 0.00 0.99 1 3897 2909
x_a[37] -1.60 0.03 1.66 0.02 0.99 1 4486 2500
x_a[38] -1.69 0.01 1.72 0.01 1.03 1 4030 2710
x_a[39] -1.64 -0.01 1.66 -0.01 1.01 1 4406 2790
x_a[40] -1.69 -0.02 1.60 -0.03 1.01 1 4144 2932
x_a[41] -1.64 0.02 1.65 0.01 1.00 1 4188 3142
x_a[42] -1.65 -0.02 1.61 -0.03 0.99 1 3666 3173
x_a[43] -1.62 -0.02 1.64 -0.01 1.00 1 3792 2291
x_a[44] -1.65 0.02 1.76 0.03 1.03 1 3759 3035
x_a[45] -1.69 0.04 1.71 0.02 1.03 1 3855 2972
x_a[46] -1.67 -0.01 1.70 0.01 1.02 1 4204 3089
x_a[47] -1.62 -0.03 1.65 -0.01 1.01 1 3883 3012
x_a[48] -1.64 0.01 1.67 0.01 1.01 1 4373 3307
x_a[49] -1.64 0.02 1.64 0.01 0.99 1 3825 3137
x_a[50] -1.65 -0.01 1.63 -0.01 1.01 1 3733 2691
x_b[1] 0.00 0.41 3.83 1.00 1.43 1 2376 1408
x_b[2] 0.00 0.47 3.73 1.00 1.39 1 2249 1424
x_b[3] 0.00 0.47 3.76 1.00 1.38 1 2846 1860
x_b[4] 0.01 0.49 3.95 1.02 1.37 1 2759 1601
x_b[5] 0.00 0.49 3.89 1.04 1.48 1 3380 1880
x_b[6] 0.00 0.46 3.81 1.00 1.38 1 2597 1389
x_b[7] 0.01 0.47 4.07 1.02 1.44 1 2836 1441
x_b[8] 0.01 0.45 3.75 1.01 1.42 1 2566 1185
x_b[9] 0.00 0.44 3.83 1.00 1.42 1 2791 1270
x_b[10] 0.00 0.48 3.78 1.01 1.40 1 2695 1392
x_b[11] 0.00 0.45 3.87 0.99 1.36 1 2813 1685
x_b[12] 0.00 0.42 3.94 1.00 1.44 1 2553 1380
x_b[13] 0.00 0.45 3.94 1.02 1.44 1 2508 1294
x_b[14] 0.00 0.44 3.71 0.97 1.39 1 2967 1638
x_b[15] 0.00 0.43 3.98 1.02 1.45 1 2518 1374
x_b[16] 0.01 0.49 3.62 1.00 1.38 1 2403 1412
x_b[17] 0.00 0.47 3.86 1.01 1.39 1 2431 1460
x_b[18] 0.00 0.46 3.79 0.98 1.38 1 2179 1358
x_b[19] 0.00 0.44 3.81 0.99 1.39 1 2985 1732
x_b[20] 0.00 0.48 3.61 0.97 1.33 1 3160 1687
x_b[21] 0.00 0.47 3.74 0.98 1.35 1 2893 1501
x_b[22] 0.00 0.45 3.85 1.02 1.48 1 2446 1468
x_b[23] 0.00 0.46 4.02 1.02 1.44 1 2781 1918
x_b[24] 0.00 0.46 3.79 1.00 1.45 1 2554 1441
x_b[25] 0.00 0.49 3.84 1.01 1.41 1 2515 1295
x_b[26] 0.00 0.44 4.08 1.03 1.51 1 3287 1667
x_b[27] 0.00 0.43 3.75 0.99 1.38 1 2447 1435
x_b[28] 0.00 0.45 3.77 1.00 1.43 1 2636 1292
x_b[29] 0.00 0.42 3.80 0.97 1.40 1 2569 1492
x_b[30] 0.00 0.45 3.82 0.99 1.42 1 2508 1632
x_b[31] 0.00 0.43 3.84 0.99 1.37 1 2815 1816
x_b[32] 0.00 0.45 3.75 1.00 1.41 1 2410 1352
x_b[33] 0.01 0.46 3.81 0.99 1.38 1 2330 1405
x_b[34] 0.00 0.46 3.89 1.01 1.49 1 2841 1492
x_b[35] 0.00 0.41 3.71 0.97 1.42 1 2538 1580
x_b[36] 0.00 0.44 3.60 0.97 1.33 1 2484 1798
x_b[37] 0.00 0.47 3.82 1.01 1.42 1 2795 1643
x_b[38] 0.00 0.47 4.12 1.04 1.49 1 2650 1492
x_b[39] 0.00 0.45 3.96 1.02 1.44 1 2596 1562
x_b[40] 0.00 0.45 3.68 0.99 1.40 1 2462 1482
x_b[41] 0.00 0.48 3.76 1.00 1.38 1 2773 1638
x_b[42] 0.00 0.46 3.95 1.01 1.43 1 2267 1182
x_b[43] 0.00 0.48 3.85 1.02 1.42 1 2523 1266
x_b[44] 0.00 0.46 3.50 0.97 1.32 1 2706 1476
x_b[45] 0.00 0.45 4.01 1.04 1.49 1 2846 1642
x_b[46] 0.00 0.45 3.94 1.00 1.42 1 2487 1658
x_b[47] 0.00 0.49 3.75 1.01 1.40 1 2537 1298
x_b[48] 0.00 0.46 3.79 0.99 1.42 1 2350 1273
x_b[49] 0.00 0.43 3.85 1.00 1.40 1 2502 1449
x_b[50] 0.00 0.46 3.78 1.01 1.44 1 2778 1413
x[1] -7.64 -0.04 7.33 -0.69 32.90 1 3894 2157
x[2] -6.02 -0.01 6.41 4.16 362.61 1 3511 1730
x[3] -6.54 0.00 6.33 0.19 33.51 1 3498 2547
x[4] -5.52 -0.02 5.74 -3.14 191.65 1 3775 2314
x[5] -6.20 -0.01 6.29 -0.47 21.58 1 4131 2630
x[6] -5.55 0.01 6.40 0.19 91.87 1 4172 2475
x[7] -5.73 0.05 6.63 -0.03 14.48 1 3751 2167
x[8] -5.80 0.01 5.58 -1.23 53.41 1 3656 2401
x[9] -5.96 0.03 6.01 -6.47 228.29 1 3569 2184
x[10] -5.73 -0.01 6.11 -1.49 88.67 1 3612 1971
x[11] -6.35 -0.05 6.08 0.98 48.99 1 3932 2519
x[12] -6.59 0.02 5.94 -0.79 30.31 1 3394 2205
x[13] -6.36 -0.01 6.24 -6.55 221.32 1 3898 2281
x[14] -6.64 0.04 6.51 3.57 149.72 1 3697 2533
x[15] -7.28 -0.01 5.90 -0.32 18.93 1 3695 2036
x[16] -5.65 -0.01 5.28 0.37 32.30 1 3912 2343
x[17] -6.10 -0.03 6.17 -0.33 26.14 1 3999 2162
x[18] -5.74 0.02 6.79 3.13 94.50 1 3862 2080
x[19] -6.01 -0.03 6.04 0.35 18.39 1 3803 2331
x[20] -5.56 0.01 6.22 -0.07 30.58 1 3877 2340
x[21] -6.21 -0.01 6.22 0.33 35.50 1 3907 2390
x[22] -6.23 0.00 6.02 -8.18 356.87 1 3685 1771
x[23] -6.17 -0.02 6.09 -0.37 14.57 1 3859 2629
x[24] -6.25 -0.02 5.56 -0.61 81.96 1 3135 2013
x[25] -6.72 0.02 5.47 1.85 202.62 1 3898 2320
x[26] -5.61 0.06 5.81 -0.34 34.70 1 3875 2847
x[27] -7.91 -0.03 6.89 6.55 323.96 1 3694 2566
x[28] -6.29 0.01 6.80 0.35 31.31 1 3828 2205
x[29] -6.57 0.02 6.30 -0.41 23.66 1 3797 2358
x[30] -5.92 0.03 6.31 -0.31 38.53 1 3879 2160
x[31] -5.80 0.00 6.28 0.36 35.66 1 3700 2567
x[32] -6.10 -0.02 6.39 -3.81 298.91 1 4374 2447
x[33] -6.00 -0.03 5.39 1.22 81.47 1 3643 2294
x[34] -5.87 0.04 6.56 0.09 30.46 1 3854 2569
x[35] -6.81 0.00 6.06 -0.07 27.08 1 3462 2259
x[36] -6.12 0.01 6.09 -0.81 40.91 1 3737 2640
x[37] -5.92 0.03 6.50 -6.16 263.64 1 4235 2372
x[38] -6.46 0.01 6.00 0.60 58.15 1 3708 2085
x[39] -6.79 -0.01 6.27 -1.86 64.47 1 4008 2319
x[40] -6.97 -0.02 5.62 0.25 146.44 1 3776 2198
x[41] -6.12 0.02 5.98 -0.88 67.65 1 3948 2554
x[42] -7.48 -0.03 6.42 -18.09 643.89 1 3637 2154
x[43] -5.99 -0.02 6.57 -0.40 52.47 1 3489 2331
x[44] -6.09 0.03 5.93 0.44 26.31 1 3772 2410
x[45] -6.25 0.04 6.65 -0.18 15.75 1 3732 2348
x[46] -5.88 -0.02 7.59 0.75 28.36 1 3577 2172
x[47] -6.18 -0.03 5.82 0.24 79.45 1 3776 2193
x[48] -6.61 0.01 6.03 -3.56 129.17 1 3800 2092
x[49] -5.88 0.02 6.52 -0.30 46.02 1 3776 2551
x[50] -7.08 0.00 6.32 -0.71 45.99 1 3489 2385
I 0.00 0.00 1.00 0.49 0.50 1 2907 2907
lp__ -95.29 -81.24 -69.52 -81.62 7.89 1 1520 2679
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).which_min_ess <- which.min(mon[101:150, 'Tail_ESS'])For all parameters, Rhat is less than 1.01 and ESS exceeds 400, indicating that sampling worked much better with this alternative parameterization. Appendix C has more results using other parameterizations of the Cauchy distribution. The vectors x_a and x_b used to form the Cauchy-distributed x have stable quantile, mean and sd values. The quantiles of each x_j are stable too, but the mean and variance estimates are widely varying.
We can further analyze potential problems using local efficiency estimates and rank plots. For this example. we take a detailed look at x[2], which had the smallest bulk-ESS of 3149.
We examine the sampling efficiency in different parts of the posterior by computing the efficiency estimates for small interval probability estimates.
plot_local_ess(fit = fit_alt1, par = which_min_ess + 100, nalpha = 20)
The efficiency estimate is good in all parts of the posterior. Further, we examine the sampling efficiency of different quantile estimates.
plot_quantile_ess(fit = fit_alt1, par = which_min_ess + 100, nalpha = 40)
The rank plots also look close to uniform across chains, which is consistent with good mixing.
samp <- as.array(fit_alt1)
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
In summary, the alternative parameterization produces results that look much better than for the nominal parameterization. There are still some differences in the tails, which we also identified via the tail-ESS.
2.1.3 Half-Cauchy with nominal parameterization
Half-Cauchy priors for non-negative parameters are common and, at least in Stan, usually specified via the nominal parameterization. In this example, we set independent half-Cauchy distributions on each element of the 50-dimensional vector \(x\) constrained to be positive (in Stan, <lower=0>). Stan then automatically switches to the unconstrained log(x) space, which changes the geometry crucially.
writeLines(readLines("half_cauchy_nom.stan"))parameters {
vector<lower=0>[50] x;
}
model {
x ~ cauchy(0, 1);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}Run the half-Cauchy with nominal parameterization and positive constraint:
fit_half_nom <- stan(file = 'half_cauchy_nom.stan', seed = 7878, refresh = 0)There are no warnings, and the sampling is much faster than for the Cauchy nominal model without the constraint.
mon <- monitor(fit_half_nom)
print(mon)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x[1] 0.08 1.01 14.11 7.41 128.72 1.00 6697 1807
x[2] 0.08 0.99 13.81 9.58 159.58 1.00 5982 1757
x[3] 0.07 0.97 13.53 4.72 30.32 1.00 7459 2290
x[4] 0.09 0.99 11.20 4.99 43.10 1.00 7919 2115
x[5] 0.07 1.01 14.51 5.05 47.10 1.00 7558 2315
x[6] 0.08 1.02 13.91 91.28 5383.88 1.00 7483 2072
x[7] 0.08 0.99 13.46 6.57 93.25 1.00 7699 2010
x[8] 0.09 1.00 11.29 4.84 34.92 1.00 9338 2097
x[9] 0.07 0.99 13.40 10.80 162.43 1.00 7521 1955
x[10] 0.08 1.02 12.53 5.61 49.28 1.00 7445 2245
x[11] 0.09 1.00 10.67 6.78 116.46 1.01 7345 2430
x[12] 0.08 1.01 12.34 5.46 47.09 1.00 8026 2040
x[13] 0.08 1.01 11.84 4.77 62.16 1.00 7166 2076
x[14] 0.08 1.01 11.97 4.99 65.23 1.00 8478 2442
x[15] 0.08 0.98 13.84 6.31 61.42 1.00 8138 2055
x[16] 0.08 0.98 11.59 6.25 67.00 1.00 7628 2347
x[17] 0.08 0.97 11.95 3.92 19.12 1.00 6984 2278
x[18] 0.08 1.00 10.98 4.05 23.61 1.00 6992 2299
x[19] 0.08 1.03 12.08 38.38 2005.23 1.00 7604 2395
x[20] 0.07 1.00 14.24 5.68 39.23 1.00 8350 2060
x[21] 0.08 1.01 11.88 6.11 110.22 1.00 8152 2797
x[22] 0.09 1.02 11.18 7.01 169.77 1.00 7609 2000
x[23] 0.07 1.00 14.22 7.35 94.96 1.00 7406 2101
x[24] 0.07 0.99 12.30 6.63 130.07 1.00 7143 2012
x[25] 0.09 1.01 11.29 9.22 343.88 1.00 7952 2305
x[26] 0.07 1.05 13.90 7.72 130.68 1.00 7070 2414
x[27] 0.07 0.99 14.12 5.75 52.77 1.00 8364 1983
x[28] 0.07 1.00 15.56 7.49 73.53 1.00 8555 2076
x[29] 0.08 1.00 13.28 5.13 37.83 1.00 7542 2031
x[30] 0.06 0.99 14.14 7.69 139.13 1.00 6432 2053
x[31] 0.08 1.04 16.07 6.73 50.89 1.00 7127 2142
x[32] 0.09 1.01 13.23 5.32 43.21 1.00 7636 2527
x[33] 0.08 1.00 12.96 6.26 92.50 1.00 7312 2178
x[34] 0.09 0.97 11.01 3.78 23.01 1.00 7409 2395
x[35] 0.07 1.00 13.56 5.08 30.58 1.00 6836 2458
x[36] 0.07 1.01 13.18 6.52 80.50 1.00 7521 1799
x[37] 0.09 1.03 11.61 4.16 23.07 1.00 7219 2363
x[38] 0.08 0.99 15.73 5.35 58.79 1.00 7492 1877
x[39] 0.07 1.00 13.93 31.05 827.68 1.00 8001 1706
x[40] 0.07 1.03 12.88 10.80 173.36 1.00 7296 1772
x[41] 0.07 0.97 13.42 7.83 96.96 1.00 7199 1964
x[42] 0.09 0.99 11.94 8.61 152.58 1.00 7515 2440
x[43] 0.09 1.01 12.12 4.59 52.98 1.00 7372 2447
x[44] 0.08 1.02 12.66 4.62 34.61 1.00 6424 2012
x[45] 0.07 1.00 13.25 20.04 914.15 1.00 6957 2100
x[46] 0.07 1.03 14.46 4.87 27.22 1.00 7806 2132
x[47] 0.08 1.00 13.13 6.86 99.30 1.00 6564 2709
x[48] 0.07 1.02 12.49 6.80 87.98 1.00 7766 2590
x[49] 0.08 1.00 12.68 5.54 37.20 1.00 7903 2137
x[50] 0.10 0.99 11.95 8.59 155.18 1.00 8049 1715
I 0.00 0.00 1.00 0.50 0.50 1.00 5802 4000
lp__ -80.63 -69.45 -59.66 -69.65 6.45 1.00 1144 2008
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).All values of Rhat are less than 1.01 and ESS exceeds 400 for all parameters, indicating good performance of the sampler despite using the nominal parameterization of the Cauchy distribution. More experiments with the half-Cauchy distribution can be found in Appendix C.
2.2 Hierarchical model: Eight Schools
This section relates to the examples presented in Section 5.2 of the paper.
The Eight Schools data is a classic example for hierarchical models (see Section 5.5 in Gelman et al., 2013), which even in its simplicity illustrates the typical problems in inference for hierarchical models. The Stan models are adapted from Michael Betancourt’s case study on Diagnosing Biased Inference with Divergences. Appendix D contains more detailed analysis of different algorithm variants.
2.2.1 A Centered Eight Schools model
writeLines(readLines("eight_schools_cp.stan"))data {
int<lower=0> J;
real y[J];
real<lower=0> sigma[J];
}
parameters {
real mu;
real<lower=0> tau;
real theta[J];
}
model {
mu ~ normal(0, 5);
tau ~ cauchy(0, 5);
theta ~ normal(mu, tau);
y ~ normal(theta, sigma);
}2.2.1.1 Centered Eight Schools model
We directly run the centered parameterization model with an increased adapt_delta value to reduce the probability of getting divergent transitions.
eight_schools <- read_rdump("eight_schools.data.R")
fit_cp <- stan(
file = 'eight_schools_cp.stan', data = eight_schools,
iter = 2000, chains = 4, seed = 483892929, refresh = 0,
control = list(adapt_delta = 0.95)
)Warning: There were 28 divergent transitions after warmup. Increasing adapt_delta above 0.95 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmupWarning: Examine the pairs() plot to diagnose sampling problemsStill, we observe a lot of divergent transitions, which in itself is already sufficient indicator of convergence problems. We can use Rhat and ESS diagnostics to recognize problematic parts of the posterior. The latter two have the advantage over the divergent transitions diagnostic that they can be used with all MCMC algorithms not only with HMC.
mon <- monitor(fit_cp)
print(mon)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -0.93 4.53 9.85 4.50 3.26 1.01 698 1125
tau 0.71 3.02 9.79 3.86 3.12 1.02 219 232
theta[1] -1.24 5.88 16.01 6.41 5.52 1.01 993 1595
theta[2] -2.44 4.99 12.84 5.05 4.73 1.00 1252 2039
theta[3] -4.99 4.32 12.13 4.06 5.36 1.00 1170 1792
theta[4] -2.91 4.88 12.73 4.89 4.77 1.00 1032 1910
theta[5] -3.99 3.95 10.80 3.75 4.59 1.00 957 2017
theta[6] -3.97 4.38 11.45 4.16 4.71 1.01 1199 1706
theta[7] -0.91 6.04 15.06 6.44 4.99 1.00 937 1589
theta[8] -3.08 4.86 13.78 4.99 5.20 1.00 1292 1498
lp__ -24.50 -15.31 -4.61 -15.03 6.07 1.02 211 242
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).See Appendix D for results of longer chains.
Bulk-ESS and Tail-ESS for the between school standard deviation tau are 219 and 232 respectively. Both are much less than 400, indicating we should investigate that parameter more carefully. We thus examine the sampling efficiency in different parts of the posterior by computing the efficiency estimate for small interval estimates for tau. These plots may either show quantiles or parameter values at the vertical axis. Red ticks show divergent transitions.
plot_local_ess(fit = fit_cp, par = "tau", nalpha = 20)
plot_local_ess(fit = fit_cp, par = "tau", nalpha = 20, rank = FALSE)
We see that the sampler has difficulties in exploring small tau values. As the sampling efficiency for estimating small tau values is practically zero, we may assume that we miss substantial amount of posterior mass and get biased estimates. Red ticks, which show iterations with divergences, have concentrated to small tau values, which gives us another indication of problems in exploring small values.
We examine also the sampling efficiency of different quantile estimates. Again, these plots may either show quantiles or parameter values at the vertical axis.
plot_quantile_ess(fit = fit_cp, par = "tau", nalpha = 40)
plot_quantile_ess(fit = fit_cp, par = "tau", nalpha = 40, rank = FALSE)
Most of the quantile estimates have worryingly low effective sample size.
Next we examine how the estimated effective sample size changes when we use more and more draws. Here we do not see sudden changes, but both bulk-ESS and tail-ESS are consistently low. See Appendix D for results of longer chains.
plot_change_ess(fit = fit_cp, par = "tau")
In line with the other findings, rank plots of tau clearly show problems in the mixing of the chains. In particular, the rank plot for the first chain indicates that it was unable to explore the lower-end of the posterior range, while the spike in the rank plot for chain 2 indicates that it spent too much time stuck in these values.
samp_cp <- as.array(fit_cp)
mcmc_hist_r_scale(samp_cp[, , "tau"])
2.2.2 Non-centered Eight Schools model
For hierarchical models, the non-centered parameterization often works better than the centered one:
writeLines(readLines("eight_schools_ncp.stan"))data {
int<lower=0> J;
real y[J];
real<lower=0> sigma[J];
}
parameters {
real mu;
real<lower=0> tau;
real theta_tilde[J];
}
transformed parameters {
real theta[J];
for (j in 1:J)
theta[j] = mu + tau * theta_tilde[j];
}
model {
mu ~ normal(0, 5);
tau ~ cauchy(0, 5);
theta_tilde ~ normal(0, 1);
y ~ normal(theta, sigma);
}For reasons of comparability, we also run the non-centered parameterization model with an increased adapt_delta value:
fit_ncp2 <- stan(
file = 'eight_schools_ncp.stan', data = eight_schools,
iter = 2000, chains = 4, seed = 483892929, refresh = 0,
control = list(adapt_delta = 0.95)
)We get zero divergences and no other warnings which is a first good sign.
mon <- monitor(fit_ncp2)
print(mon)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.00 4.41 9.80 4.43 3.29 1 5199 3093
tau 0.24 2.73 9.46 3.56 3.17 1 2672 1967
theta_tilde[1] -1.35 0.30 1.96 0.31 1.00 1 5589 2535
theta_tilde[2] -1.45 0.11 1.63 0.10 0.92 1 5605 2956
theta_tilde[3] -1.70 -0.09 1.60 -0.08 1.00 1 5600 2863
theta_tilde[4] -1.44 0.09 1.63 0.08 0.93 1 5806 3246
theta_tilde[5] -1.66 -0.17 1.39 -0.16 0.92 1 5021 3165
theta_tilde[6] -1.60 -0.07 1.54 -0.06 0.96 1 5051 2866
theta_tilde[7] -1.31 0.34 1.88 0.33 0.98 1 4559 2754
theta_tilde[8] -1.50 0.07 1.66 0.08 0.96 1 5634 2990
theta[1] -1.78 5.66 16.24 6.19 5.70 1 4330 3161
theta[2] -2.33 4.83 12.35 4.89 4.54 1 5314 3418
theta[3] -5.07 4.17 11.92 3.99 5.33 1 4353 3211
theta[4] -2.77 4.76 12.52 4.85 4.76 1 5685 3159
theta[5] -4.41 3.85 10.59 3.64 4.61 1 4822 3416
theta[6] -3.82 4.28 11.45 4.07 4.80 1 4863 2816
theta[7] -1.18 5.86 15.20 6.26 5.01 1 4743 3570
theta[8] -3.36 4.78 12.93 4.89 5.15 1 5094 3335
lp__ -11.20 -6.67 -3.76 -6.97 2.35 1 1488 2254
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).All Rhat < 1.01 and ESS > 400 indicate a much better performance of the non-centered parameterization.
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates for tau.
plot_local_ess(fit = fit_ncp2, par = 2, nalpha = 20)
Small tau values are still more difficult to explore, but the relative efficiency is good. We may also check this with a finer resolution:
plot_local_ess(fit = fit_ncp2, par = 2, nalpha = 50)
The sampling efficiency for different quantile estimates looks good as well.
plot_quantile_ess(fit = fit_ncp2, par = 2, nalpha = 40)
The rank plots of tau show no substantial differences between chains.
samp_ncp2 <- as.array(fit_ncp2)
mcmc_hist_r_scale(samp_ncp2[, , 2])
References
Betancourt, M. (2017) ‘A conceptual introduction to hamiltonian monte carlo’, arXiv preprint arXiv:1701.02434.
Brooks, S. P. and Gelman, A. (1998) ‘General methods for monitoring convergence of iterative simulations’, Journal of Computational and Graphical Statistics, 7(4), pp. 434–455.
Gelman, A. et al. (2013) Bayesian data analysis, third edition. CRC Press.
Appendices
The following abbreviations are used throughout the appendices:
- N = total number of draws
- Rhat = classic no-split-Rhat
- sRhat = classic split-Rhat
- zsRhat = rank-normalized split-Rhat
- all chains are jointly ranked and z-transformed
- can detect differences in location and trends
- zfsRhat = rank-normalized folded split-Rhat
- all chains are jointly “folded” by computing absolute deviation from median, ranked and z-transformed
- can detect differences in scales
- seff = no-split effective sample size
- reff = seff / N
- zsseff = rank-normalized split effective sample size
- estimates the efficiency of mean estimate for rank normalized values
- zsreff = zsseff / N
- zfsseff = rank-normalized folded split effective sample size
- estimates the efficiency of rank normalized mean absolute deviation
- zfsreff = zfsseff / N
- tailseff = minimum of rank-normalized split effective sample sizes of the 5% and 95% quantiles
- tailreff = tailseff / N
- medsseff = median split effective sample size
- estimates the efficiency of the median
- medsreff = medsseff / N
- madsseff = mad split effective sample size
- estimates the efficiency of the median absolute deviation
- madsreff = madsseff / N
Appendix A: Computing the effective sample size
There are no examples or numerical experiments related to Appendix A in the paper.
Appendix B: Normal distributions with additional trend, shift or scaling
This part focuses on diagnostics for
- all chains having a trend and a similar marginal distribution
- one of the chains having a different mean
- one of the chains having a lower marginal variance
To simplify, in this part, independent draws are used as a proxy for very efficient MCMC sampling. First, we sample draws from a standard-normal distribution. We will discuss the behavior for non-normal distributions later. See Appendix A for the abbreviations used.
Adding the same trend to all chains
All chains are from the same Normal(0, 1) distribution plus a linear trend added to all chains:
conds <- expand.grid(
iters = c(250, 1000, 4000),
trend = c(0, 0.25, 0.5, 0.75, 1),
rep = 1:10
)
res <- vector("list", nrow(conds))
chains = 4
for (i in 1:nrow(conds)) {
iters <- conds[i, "iters"]
trend <- conds[i, "trend"]
rep <- conds[i, "rep"]
r <- array(rnorm(iters * chains), c(iters, chains))
r <- r + seq(-trend, trend, length.out = iters)
rs <- as.data.frame(monitor_extra(r))
res[[i]] <- cbind(iters, trend, rep, rs)
}
res <- bind_rows(res)If we don’t split chains, Rhat misses the trends if all chains still have a similar marginal distribution.
ggplot(data = res, aes(y = Rhat, x = trend)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = 1.005, linetype = 'dashed') +
geom_hline(yintercept = 1) +
ggtitle('Rhat without splitting chains')
Split-Rhat can detect trends, even if the marginals of the chains are similar.
ggplot(data = res, aes(y = zsRhat, x = trend)) +
geom_point() + geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = 1.005, linetype = 'dashed') +
geom_hline(yintercept = 1) +
ggtitle('Split-Rhat')
Result: Split-Rhat is useful for detecting non-stationarity (i.e., trends) in the chains. If we use a threshold of \(1.01\), we can detect trends which account for 2% or more of the total marginal variance. If we use a threshold of \(1.1\), we detect trends which account for 30% or more of the total marginal variance.
The effective sample size is based on split Rhat and within-chain autocorrelation. We plot the relative efficiency \(R_{\rm eff}=S_{\rm eff}/S\) for easier comparison between different values of \(S\). In the plot below, dashed lines indicate the threshold at which we would consider the effective sample size to be sufficient (i.e., \(S_{\rm eff} > 400\)). Since we plot the relative efficiency instead of the effective sample size itself, this threshold is divided by \(S\), which we compute here as the number of iterations per chain (variable iter) times the number of chains (\(4\)).
ggplot(data = res, aes(y = zsreff, x = trend)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = c(0,1)) +
geom_hline(aes(yintercept = 400 / (4 * iters)), linetype = 'dashed') +
ggtitle('Relative Bulk-ESS (zsreff)') +
scale_y_continuous(breaks = seq(0, 1.5, by = 0.25))
Result: Split-Rhat is more sensitive to trends for small sample sizes, but effective sample size becomes more sensitive for larger samples sizes (as autocorrelations can be estimated more accurately).
Advice: If in doubt, run longer chains for more accurate convergence diagnostics.
Shifting one chain
Next we investigate the sensitivity to detect if one of the chains has not converged to the same distribution as the others, but has a different mean.
conds <- expand.grid(
iters = c(250, 1000, 4000),
shift = c(0, 0.25, 0.5, 0.75, 1),
rep = 1:10
)
res <- vector("list", nrow(conds))
chains = 4
for (i in 1:nrow(conds)) {
iters <- conds[i, "iters"]
shift <- conds[i, "shift"]
rep <- conds[i, "rep"]
r <- array(rnorm(iters * chains), c(iters, chains))
r[, 1] <- r[, 1] + shift
rs <- as.data.frame(monitor_extra(r))
res[[i]] <- cbind(iters, shift, rep, rs)
}
res <- bind_rows(res)ggplot(data = res, aes(y = zsRhat, x = shift)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = 1.005, linetype = 'dashed') +
geom_hline(yintercept = 1) +
ggtitle('Split-Rhat')
Result: If we use a threshold of \(1.01\), we can detect shifts with a magnitude of one third or more of the marginal standard deviation. If we use a threshold of \(1.1\), we detect shifts with a magnitude equal to or larger than the marginal standard deviation.
ggplot(data = res, aes(y = zsreff, x = shift)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = c(0,1)) +
geom_hline(aes(yintercept = 400 / (4 * iters)), linetype = 'dashed') +
ggtitle('Relative Bulk-ESS (zsreff)') +
scale_y_continuous(breaks = seq(0, 1.5, by = 0.25))
Result: The effective sample size is not as sensitive, but a shift with a magnitude of half the marginal standard deviation or more will lead to very low relative efficiency when the total number of draws increases.
Rank plots can be used to visualize differences between chains. Here, we show rank plots for the case of 4 chains, 250 draws per chain, and a shift of 0.5.
iters = 250
chains = 4
shift = 0.5
r <- array(rnorm(iters * chains), c(iters, chains))
r[, 1] <- r[, 1] + shift
colnames(r) <- 1:4
mcmc_hist_r_scale(r)
Although, Rhat was less than \(1.05\) for this situation, the rank plots clearly show that the first chains behaves differently.
Scaling one chain
Next, we investigate the sensitivity to detect if one of the chains has not converged to the same distribution as the others, but has lower marginal variance.
conds <- expand.grid(
iters = c(250, 1000, 4000),
scale = c(0, 0.25, 0.5, 0.75, 1),
rep = 1:10
)
res <- vector("list", nrow(conds))
chains = 4
for (i in 1:nrow(conds)) {
iters <- conds[i, "iters"]
scale <- conds[i, "scale"]
rep <- conds[i, "rep"]
r <- array(rnorm(iters * chains), c(iters, chains))
r[, 1] <- r[, 1] * scale
rs <- as.data.frame(monitor_extra(r))
res[[i]] <- cbind(iters, scale, rep, rs)
}
res <- bind_rows(res)We first look at the Rhat estimates:
ggplot(data = res, aes(y = zsRhat, x = scale)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = 1.005, linetype = 'dashed') +
geom_hline(yintercept = 1) +
ggtitle('Split-Rhat')
Result: Split-Rhat is not able to detect scale differences between chains.
ggplot(data = res, aes(y = zfsRhat, x = scale)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = 1.005, linetype = 'dashed') +
geom_hline(yintercept = 1) +
ggtitle('Folded-split-Rhat')
Result: Folded-Split-Rhat focuses on scales and detects scale differences.
Result: If we use a threshold of \(1.01\), we can detect a chain with scale less than \(3/4\) of the standard deviation of the others. If we use threshold of \(1.1\), we detect a chain with standard deviation less than \(1/4\) of the standard deviation of the others.
Next, we look at the effective sample size estimates:
ggplot(data = res, aes(y = zsreff, x = scale)) +
geom_point() +
geom_jitter() +
facet_grid(. ~ iters) +
geom_hline(yintercept = c(0,1)) +
geom_hline(aes(yintercept = 400 / (4 * iters)), linetype = 'dashed') +
ggtitle('Relative Bulk-ESS (zsreff)') +
scale_y_continuous(breaks = seq(0, 1.5, by = 0.25))
Result: The bulk effective sample size of the mean does not see a problem as it focuses on location differences between chains.
Rank plots can be used to visualize differences between chains. Here, we show rank plots for the case of 4 chains, 250 draws per chain, and with one chain having a standard deviation of 0.75 as opposed to a standard deviation of 1 for the other chains.
iters = 250
chains = 4
scale = 0.75
r <- array(rnorm(iters * chains), c(iters, chains))
r[, 1] <- r[, 1] * scale
colnames(r) <- 1:4
mcmc_hist_r_scale(r)
Although folded Rhat is \(1.06\), the rank plots clearly show that the first chains behaves differently.
Appendix C: More experiments with the Cauchy distribution
The classic split-Rhat is based on calculating within and between chain variances. If the marginal distribution of a chain is such that the variance is not defined (i.e. infinite), the classic split-Rhat is not well justified. In this section, we will use the Cauchy distribution as an example of such distribution. Also in cases where mean and variance are finite, the distribution can be far from Gaussian. Especially distributions with very long tails cause instability for variance and autocorrelation estimates. To alleviate these problems we will use Split-Rhat for rank-normalized draws.
The following Cauchy models are from Michael Betancourt’s case study Fitting The Cauchy Distribution
Nominal parameterization of Cauchy
We already looked at the nominal Cauchy model with direct parameterization in the main text, but for completeness, we take a closer look using different variants of the diagnostics.
writeLines(readLines("cauchy_nom.stan"))parameters {
vector[50] x;
}
model {
x ~ cauchy(0, 1);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}Default Stan options
Run the nominal model:
fit_nom <- stan(file = 'cauchy_nom.stan', seed = 7878, refresh = 0)Warning: There were 1421 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceededWarning: Examine the pairs() plot to diagnose sampling problemsTreedepth exceedence and Bayesian Fraction of Missing Information are dynamic HMC specific diagnostics (Betancourt, 2017). We get warnings about very large number of transitions after warmup that exceeded the maximum treedepth, which is likely due to very long tails of the Cauchy distribution. All chains have low estimated Bayesian fraction of missing information also indicating slow mixing.
Trace plots for the first parameter look wild with occasional large values:
samp <- as.array(fit_nom)
mcmc_trace(samp[, , 1])
Let’s check Rhat and ESS diagnostics.
res <- monitor_extra(samp[, , 1:50])
which_min_ess <- which.min(res$tailseff)
plot_rhat(res)
For one parameter, Rhats exceed the classic threshold of 1.1. Depending on the Rhat estimate, a few others also exceed the threshold of 1.01. The rank normalized split-Rhat has several values over 1.01. Please note that the classic split-Rhat is not well defined in this example, because mean and variance of the Cauchy distribution are not finite.
plot_ess(res) 
Both classic and new effective sample size estimates have several very small values, and so the overall sample shouldn’t be trusted.
Result: Effective sample size is more sensitive than (rank-normalized) split-Rhat to detect problems of slow mixing.
We also check the log posterior value lp__ and find out that the effective sample size is worryingly low.
res <- monitor_extra(samp[, , 51:52])
cat('lp__: Bulk-ESS = ', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 252 cat('lp__: Tail-ESS = ', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 453 We can further analyze potential problems using local effective sample size and rank plots. We examine x[49], which has the smallest tail-ESS of 252.
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates (see Section Efficiency for small interval probability estimates). Each interval contains \(1/k\) of the draws (e.g., with \(k=20\)). The small interval efficiency measures mixing of an indicator function which indicates when the values are inside the specific small interval. This gives us a local efficiency measure which does not depend on the shape of the distribution.
plot_local_ess(fit = fit_nom, par = which_min_ess, nalpha = 20)
We see that the efficiency is worryingly low in the tails (which is caused by slow mixing in long tails of Cauchy). Orange ticks show draws that exceeded the maximum treedepth.
An alternative way to examine the effective sample size in different parts of the posterior is to compute effective sample size for quantiles (see Section Efficiency for quantiles). Each interval has a specified proportion of draws, and the efficiency measures mixing of an indicator function’s results which indicate when the values are inside the specific interval.
plot_quantile_ess(fit = fit_nom, par = which_min_ess, nalpha = 40)
We see that the efficiency is worryingly low in the tails (which is caused by slow mixing in long tails of Cauchy). Orange ticks show draws that exceeded the maximum treedepth.
We can further analyze potential problems using rank plots, from which we clearly see differences between chains.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
Default Stan options + increased maximum treedepth
We can try to improve the performance by increasing max_treedepth to \(20\):
fit_nom_td20 <- stan(
file = 'cauchy_nom.stan', seed = 7878,
refresh = 0, control = list(max_treedepth = 20)
)Warning: There were 1 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 20. See
http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceededWarning: Examine the pairs() plot to diagnose sampling problemsTrace plots for the first parameter still look wild with occasional large values.
samp <- as.array(fit_nom_td20)
mcmc_trace(samp[, , 1])
res <- monitor_extra(samp[, , 1:50])
which_min_ess <- which.min(res$tailseff)We check the diagnostics for all \(x\).
plot_rhat(res)
All Rhats are below \(1.1\), but many are over \(1.01\). Classic split-Rhat has more variation than the rank normalized Rhat (note that the former is not well defined). The folded rank normalized Rhat shows that there is still more variation in the scale than in the location between different chains.
plot_ess(res) 
Some classic effective sample sizes are very small. If we wouldn’t realize that the variance is infinite, we might try to run longer chains, but in case of an infinite variance, zero relative efficiency (ESS/S) is the truth and longer chains won’t help with that. However other quantities can be well defined, and that’s why it is useful to also look at the rank normalized version as a generic transformation to achieve finite mean and variance. The smallest bulk-ESS is less than 1000, which is not that bad. The smallest median-ESS is larger than 2500, that is we are able to estimate the median efficiently. However, many tail-ESS’s are less than 400 indicating problems for estimating the scale of the posterior.
Result: The rank normalized effective sample size is more stable than classic effective sample size, which is not well defined for the Cauchy distribution.
Result: It is useful to look at both bulk- and tail-ESS.
We check also lp__. Although increasing max_treedepth improved bulk-ESS of x, the efficiency for lp__ didn’t change.
res <- monitor_extra(samp[, , 51:52])
cat('lp__: Bulk-ESS =', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 158 cat('lp__: Tail-ESS =', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 481 We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit = fit_nom_td20, par = which_min_ess, nalpha = 20)
It seems that increasing max_treedepth has not much improved the efficiency in the tails. We also examine the effective sample size of different quantile estimates.
plot_quantile_ess(fit = fit_nom_td20, par = which_min_ess, nalpha = 40)
The rank plot visualisation of x[8], which has the smallest tail-ESS of 481 among the \(x\), indicates clear convergence problems.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
The rank plot visualisation of lp__, which has an effective sample size 158, doesn’t look so good either.
mcmc_hist_r_scale(samp[, , "lp__"])
Default Stan options + increased maximum treedepth + longer chains
Let’s try running 8 times longer chains.
fit_nom_td20l <- stan(
file = 'cauchy_nom.stan', seed = 7878,
refresh = 0, control = list(max_treedepth = 20),
warmup = 1000, iter = 9000
)Warning: There were 2 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 20. See
http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceededWarning: Examine the pairs() plot to diagnose sampling problemsTrace plots for the first parameter still look wild with occasional large values.
samp <- as.array(fit_nom_td20l)
mcmc_trace(samp[, , 1])
res <- monitor_extra(samp[, , 1:50])
which_min_ess <- which.min(res$tailseff)Let’s check the diagnostics for all \(x\).
plot_rhat(res)
All Rhats are below \(1.01\). The classic split-Rhat has more variation than the rank normalized Rhat (note that the former is not well defined in this case).
plot_ess(res) 
Most classic ESS’s are close to zero. Running longer chains just made most classic ESS’s even smaller.
The smallest bulk-ESS are around 5000, which is not that bad. The smallest median-ESS’s are larger than 25000, that is we are able to estimate the median efficiently. However, the smallest tail-ESS is 833 indicating problems for estimating the scale of the posterior.
Result: The rank normalized effective sample size is more stable than classic effective sample size even for very long chains.
Result: It is useful to look at both bulk- and tail-ESS.
We also check lp__. Although increasing the number of iterations improved bulk-ESS of the \(x\), the relative efficiency for lp__ didn’t change.
res <- monitor_extra(samp[, , 51:52])
cat('lp__: Bulk-ESS =', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 1289 cat('lp__: Tail-ESS =', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 2108 We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit = fit_nom_td20l, par = which_min_ess, nalpha = 20)
Increasing the chain length did not seem to change the relative efficiency. With more draws from the longer chains we can use a finer resolution for the local efficiency estimates.
plot_local_ess(fit = fit_nom_td20l, par = which_min_ess, nalpha = 100)
The sampling efficiency far in the tails is worryingly low. This was more difficult to see previously with less draws from the tails. We see similar problems in the plot of effective sample size for quantiles.
plot_quantile_ess(fit = fit_nom_td20l, par = which_min_ess, nalpha = 100)
Let’s look at the rank plot visualisation of x[48], which has the smallest tail-ESS 2108 among the \(x\).
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
Increasing the number of iterations couldn’t remove the mixing problems at the tails. The mixing problem is inherent to the nominal parameterization of Cauchy distribution.
First alternative parameterization of the Cauchy distribution
Next, we examine an alternative parameterization and consider the Cauchy distribution as a scale mixture of Gaussian distributions. The model has two parameters and the Cauchy distributed \(x\) can be computed from those. In addition to improved sampling performance, the example illustrates that focusing on diagnostics matters.
writeLines(readLines("cauchy_alt_1.stan"))parameters {
vector[50] x_a;
vector<lower=0>[50] x_b;
}
transformed parameters {
vector[50] x = x_a ./ sqrt(x_b);
}
model {
x_a ~ normal(0, 1);
x_b ~ gamma(0.5, 0.5);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}We run the alternative model:
fit_alt1 <- stan(file='cauchy_alt_1.stan', seed=7878, refresh = 0)There are no warnings and the sampling is much faster.
samp <- as.array(fit_alt1)
res <- monitor_extra(samp[, , 101:150])
which_min_ess <- which.min(res$tailseff)plot_rhat(res)
All Rhats are below \(1.01\). Classic split-Rhats also look good even though they are not well defined for the Cauchy distribution.
plot_ess(res) 
Result: Rank normalized ESS’s have less variation than classic one which is not well defined for Cauchy.
We check lp__:
res <- monitor_extra(samp[, , 151:152])
cat('lp__: Bulk-ESS =', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 1520 cat('lp__: Tail-ESS =', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 2679 The relative efficiencies for lp__ are also much better than with the nominal parameterization.
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit = fit_alt1, par = 100 + which_min_ess, nalpha = 20)
The effective sample size is good in all parts of the posterior. We also examine the effective sample size of different quantile estimates.
plot_quantile_ess(fit = fit_alt1, par = 100 + which_min_ess, nalpha = 40)
We compare the mean relative efficiencies of the underlying parameters in the new parameterization and the actual \(x\) we are interested in.
res <- monitor_extra(samp[, , 101:150])
res1 <- monitor_extra(samp[, , 1:50])
res2 <- monitor_extra(samp[, , 51:100])cat('Mean Bulk-ESS for x =' , round(mean(res[, 'zsseff']), 2), '\n')Mean Bulk-ESS for x = 3773.76 cat('Mean Tail-ESS for x =' , round(mean(res[, 'tailseff']), 2), '\n')Mean Tail-ESS for x = 2312.1 cat('Mean Bulk-ESS for x_a =' , round(mean(res1[, 'zsseff']), 2), '\n')Mean Bulk-ESS for x_a = 4032.3 cat('Mean Bulk-ESS for x_b =' , round(mean(res2[, 'zsseff']), 2), '\n')Mean Bulk-ESS for x_b = 2638.76 Result: We see that the effective sample size of the interesting \(x\) can be different from the effective sample size of the parameters \(x_a\) and \(x_b\) that we used to compute it.
The rank plot visualisation of x[2], which has the smallest tail-ESS of 1730 among the \(x\) looks better than for the nominal parameterization.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
Similarly, the rank plot visualisation of lp__, which has a relative efficiency of -81.62, 0.2, 7.89, -95.29, -81.24, -69.52, 1498, 0.37, 1511, 1507, 1520, 0.38, 1, 1, 1, 1, 1, 2933, 0.73, 2679, 0.67, 1942, 0.49, 2956, 0.74 looks better than for the nominal parameterization.
mcmc_hist_r_scale(samp[, , "lp__"])
Another alternative parameterization of the Cauchy distribution
Another alternative parameterization is obtained by a univariate transformation as shown in the following code (see also the 3rd alternative in Michael Betancourt’s case study).
writeLines(readLines("cauchy_alt_3.stan"))parameters {
vector<lower=0, upper=1>[50] x_tilde;
}
transformed parameters {
vector[50] x = tan(pi() * (x_tilde - 0.5));
}
model {
// Implicit uniform prior on x_tilde
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}We run the alternative model:
fit_alt3 <- stan(file='cauchy_alt_3.stan', seed=7878, refresh = 0)There are no warnings, and the sampling is much faster than for the nominal model.
samp <- as.array(fit_alt3)
res <- monitor_extra(samp[, , 51:100])
which_min_ess <- which.min(res$tailseff)plot_rhat(res)
All Rhats except some folded Rhats are below 1.01. Classic split-Rhat’s look also good even though it is not well defined for the Cauchy distribution.
plot_ess(res) 
Result: Rank normalized relative efficiencies have less variation than classic ones. Bulk-ESS and median-ESS are slightly larger than 1, which is possible for antithetic Markov chains which have negative correlation for odd lags.
We also take a closer look at the lp__ value:
res <- monitor_extra(samp[, , 101:102])
cat('lp__: Bulk-ESS =', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 1338 cat('lp__: Tail-ESS =', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 2065 The effective sample size for these are also much better than with the nominal parameterization.
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit = fit_alt3, par = 50 + which_min_ess, nalpha = 20)
We examine also the sampling efficiency of different quantile estimates.
plot_quantile_ess(fit = fit_alt3, par = 50 + which_min_ess, nalpha = 40)
The effective sample size in tails is worse than for the first alternative parameterization, although it’s still better than for the nominal parameterization.
We compare the mean effective sample size of the underlying parameter in the new parameterization and the actually Cauchy distributed \(x\) we are interested in.
res <- monitor_extra(samp[, , 51:100])
res1 <- monitor_extra(samp[, , 1:50])
cat('Mean bulk-seff for x =' , round(mean(res[, 'zsseff']), 2), '\n')Mean bulk-seff for x = 4868.4 cat('Mean tail-seff for x =' , round(mean(res[, 'zfsseff']), 2), '\n')Mean tail-seff for x = 1604.48 cat('Mean bulk-seff for x_tilde =' , round(mean(res1[, 'zsseff']), 2), '\n')Mean bulk-seff for x_tilde = 4868.4 cat('Mean tail-seff for x_tilde =' , round(mean(res1[, 'zfsseff']), 2), '\n')Mean tail-seff for x_tilde = 1612.3 The Rank plot visualisation of x[23], which has the smallest tail-ESS of 1925 among the \(x\) reveals shows good efficiency, similar to the results for lp__.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
mcmc_hist_r_scale(samp[, , "lp__"])
Half-Cauchy distribution with nominal parameterization
Half-Cauchy priors are common and, for example, in Stan usually set using the nominal parameterization. However, when the constraint <lower=0> is used, Stan does the sampling automatically in the unconstrained log(x) space, which changes the geometry crucially.
writeLines(readLines("half_cauchy_nom.stan"))parameters {
vector<lower=0>[50] x;
}
model {
x ~ cauchy(0, 1);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}We run the half-Cauchy model with nominal parameterization (and positive constraint).
fit_half_nom <- stan(file = 'half_cauchy_nom.stan', seed = 7878, refresh = 0)There are no warnings and the sampling is much faster than for the full Cauchy distribution with nominal parameterization.
samp <- as.array(fit_half_nom)
res <- monitor_extra(samp[, , 1:50])
which_min_ess <- which.min(res$tailseff)plot_rhat(res) 
All Rhats are below \(1.01\). Classic split-Rhats also look good even though they are not well defined for the half-Cauchy distribution.
plot_ess(res) 
Result: Rank normalized effective sample size have less variation than classic ones. Some Bulk-ESS and median-ESS are larger than 1, which is possible for antithetic Markov chains which have negative correlation for odd lags.
Due to the <lower=0> constraint, the sampling was made in the log(x) space, and we can also check the performance in that space.
res <- monitor_extra(log(samp[, , 1:50]))plot_ess(res) 
\(\log(x)\) is quite close to Gaussian, and thus classic effective sample size is also close to rank normalized ESS which is exactly the same as for the original \(x\) as rank normalization is invariant to bijective transformations.
Result: The rank normalized effective sample size is close to the classic effective sample size for transformations which make the distribution close to Gaussian.
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit = fit_half_nom, par = which_min_ess, nalpha = 20)
The effective sample size is good overall, with only a small dip in tails. We can also examine the effective sample size of different quantile estimates.
plot_quantile_ess(fit = fit_half_nom, par = which_min_ess, nalpha = 40)
The rank plot visualisation of x[39], which has the smallest tail-ESS of 1706 among \(x\), looks good.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
The rank plot visualisation of lp__ reveals some small differences in the scales, but it’s difficult to know whether this small variation from uniform is relevant.
mcmc_hist_r_scale(samp[, , "lp__"])
Alternative parameterization of the half-Cauchy distribution
writeLines(readLines("half_cauchy_alt.stan"))parameters {
vector<lower=0>[50] x_a;
vector<lower=0>[50] x_b;
}
transformed parameters {
vector[50] x = x_a .* sqrt(x_b);
}
model {
x_a ~ normal(0, 1);
x_b ~ inv_gamma(0.5, 0.5);
}
generated quantities {
real I = fabs(x[1]) < 1 ? 1 : 0;
}Run half-Cauchy with alternative parameterization
fit_half_reparam <- stan(
file = 'half_cauchy_alt.stan', seed = 7878, refresh = 0
)There are no warnings and the sampling is as fast for the half-Cauchy nominal model.
samp <- as.array(fit_half_reparam)
res <- monitor_extra(samp[, , 101:150])
which_min_ess <- which.min(res$tailseff)plot_rhat(res)
plot_ess(res) 
Result: The Rank normalized relative efficiencies have less variation than classic ones which is not well defined for the Cauchy distribution. Based on bulk-ESS and median-ESS, the efficiency for central quantities is much lower, but based on tail-ESS and MAD-ESS, the efficiency in the tails is slightly better than for the half-Cauchy distribution with nominal parameterization. We also see that a parameterization which is good for the full Cauchy distribution is not necessarily good for the half-Cauchy distribution as the <lower=0> constraint additionally changes the parameterization.
We also check the lp__ values:
res <- monitor_extra(samp[, , 151:152])
cat('lp__: Bulk-ESS =', round(res['lp__', 'zsseff'], 2), '\n')lp__: Bulk-ESS = 735 cat('lp__: Tail-ESS =', round(res['lp__', 'tailseff'], 2), '\n')lp__: Tail-ESS = 1511 We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small interval probability estimates.
plot_local_ess(fit_half_reparam, par = 100 + which_min_ess, nalpha = 20)
We also examine the effective sample size for different quantile estimates.
plot_quantile_ess(fit_half_reparam, par = 100 + which_min_ess, nalpha = 40)
The effective sample size near zero is much worse than for the half-Cauchy distribution with nominal parameterization.
The Rank plot visualisation of x[13], which has the smallest tail-ESS of 1511 among the \(x\), reveals deviations from uniformity, which is expected with lower effective sample size.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp[, , xmin])
A similar result is obtained when looking at the rank plots of lp__.
mcmc_hist_r_scale(samp[, , "lp__"])
The Cauchy distribution with Jags
So far, we have run all models in Stan, but we want to also investigate whether similar problems arise with probabilistic programming languages that use other samplers than variants of Hamiltonian Monte-Carlo. Thus, we will fit the eight schools models also with Jags, which uses a dialect of the BUGS language to specify models. Jags uses a clever mix of Gibbs and Metropolis-Hastings sampling. This kind of sampling does not scale well to high dimensional posteriors of strongly interdependent parameters, but for the relatively simple models discussed in this case study it should work just fine.
The Jags code for the nominal parameteriztion of the cauchy distribution looks as follows:
writeLines(readLines("cauchy_nom.bugs"))model {
for (i in 1:50) {
x[i] ~ dt(0, 1, 1)
}
}First, we initialize the Jags model for reusage later.
jags_nom <- jags.model(
"cauchy_nom.bugs",
n.chains = 4, n.adapt = 10000
)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 0
Unobserved stochastic nodes: 50
Total graph size: 52
Initializing modelNext, we sample 1000 iterations for each of the 4 chains for easy comparison with the corresponding Stan results.
samp_jags_nom <- coda.samples(
jags_nom, variable.names = "x",
n.iter = 1000
)
samp_jags_nom <- aperm(abind(samp_jags_nom, along = 3), c(1, 3, 2))
dimnames(samp_jags_nom)[[2]] <- paste0("chain:", 1:4)We summarize the model as follows:
mon <- monitor(samp_jags_nom)
print(mon)Inference for the input samples (4 chains: each with iter = 1000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x[1] -6.93 0.02 6.51 -0.55 53.25 1 3830 3932
x[2] -7.21 0.02 5.87 -0.72 60.59 1 3902 3853
x[3] -6.06 0.04 6.68 0.94 50.81 1 3905 3747
x[4] -5.71 0.01 6.29 1.45 64.58 1 3837 3893
x[5] -5.75 0.01 6.50 4.60 265.33 1 3878 3648
x[6] -5.80 -0.05 6.36 1.58 122.00 1 3947 3933
x[7] -6.27 0.05 7.02 0.04 101.95 1 4052 3891
x[8] -6.82 0.02 6.27 61.47 3828.43 1 4121 4045
x[9] -5.79 0.01 6.28 1.01 58.57 1 3885 3774
x[10] -6.58 -0.01 6.03 0.71 59.11 1 3891 3838
x[11] -6.24 0.01 6.41 0.57 29.92 1 3795 3645
x[12] -6.15 -0.01 5.97 -1.21 36.69 1 4163 4011
x[13] -5.67 -0.01 6.61 -1.56 37.27 1 4113 4056
x[14] -6.42 -0.04 5.93 -1.21 112.06 1 3922 3877
x[15] -6.19 0.00 5.67 -2.54 145.67 1 4244 3839
x[16] -6.06 -0.01 5.78 -0.67 32.09 1 3740 3703
x[17] -6.06 0.02 7.02 0.16 92.19 1 3867 3785
x[18] -6.43 -0.01 5.87 -0.62 32.15 1 3854 3848
x[19] -7.24 0.02 5.98 -1.40 67.31 1 3925 4013
x[20] -6.68 -0.02 6.15 -0.81 45.61 1 3837 3866
x[21] -5.82 0.00 7.09 -1.03 61.63 1 3814 3870
x[22] -5.98 0.05 7.38 0.92 67.15 1 3667 3799
x[23] -6.41 -0.01 6.59 7.93 504.67 1 3850 4036
x[24] -5.67 -0.02 7.09 -0.22 38.16 1 3600 3844
x[25] -5.66 0.05 6.35 0.11 19.05 1 4106 3969
x[26] -6.22 0.01 6.97 -1.43 55.55 1 3588 3986
x[27] -6.75 0.00 6.89 1.40 474.30 1 3715 4094
x[28] -6.33 0.03 6.90 1.18 117.64 1 3718 3865
x[29] -6.03 0.01 6.87 -2.94 281.30 1 4012 3802
x[30] -6.76 -0.04 5.79 0.74 39.61 1 4024 3872
x[31] -5.51 0.01 6.40 0.03 50.73 1 3702 3763
x[32] -6.72 0.00 6.53 -1.34 95.60 1 4084 3865
x[33] -6.65 0.02 5.61 -0.28 34.47 1 3636 3292
x[34] -6.78 0.01 6.92 -0.59 44.36 1 3820 3890
x[35] -6.61 -0.02 6.84 1.06 39.50 1 4129 3787
x[36] -6.19 0.00 6.73 1.51 58.17 1 4245 3789
x[37] -6.22 0.00 5.98 5.07 236.63 1 3999 3931
x[38] -6.86 -0.01 6.40 -0.24 35.80 1 3701 3888
x[39] -6.61 -0.01 6.02 0.54 60.11 1 4140 3882
x[40] -6.25 0.00 5.82 -0.65 27.90 1 3834 3930
x[41] -6.37 -0.01 6.06 1.01 138.14 1 3721 3507
x[42] -6.28 0.04 6.69 -0.42 22.08 1 4102 4015
x[43] -5.71 0.00 5.95 1.18 87.59 1 4171 3730
x[44] -5.76 -0.03 6.01 0.95 31.31 1 4079 3949
x[45] -6.19 -0.05 6.39 -3.06 217.64 1 3655 3847
x[46] -6.70 0.00 7.10 0.69 48.83 1 3912 3775
x[47] -5.67 0.06 7.08 0.52 23.83 1 4000 3855
x[48] -6.56 -0.02 5.85 -0.69 45.41 1 3866 3893
x[49] -5.91 0.03 6.79 -0.30 79.41 1 4116 3971
x[50] -6.24 -0.01 5.96 -1.49 90.43 1 3764 3682
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).which_min_ess <- which.min(mon[1:50, 'Bulk_ESS'])The overall results look very promising with Rhats = 1 and ESS values close to the total number of draws of 4000. We take a detailed look at x[26], which has the smallest bulk-ESS of 3588.
We examine the sampling efficiency in different parts of the posterior by computing the efficiency estimates for small interval probability estimates.
plot_local_ess(fit = samp_jags_nom, par = which_min_ess, nalpha = 20)
The efficiency estimate is good in all parts of the posterior. Further, we examine the sampling efficiency of different quantile estimates.
plot_quantile_ess(fit = samp_jags_nom, par = which_min_ess, nalpha = 40)
Rank plots also look rather similar across chains.
xmin <- paste0("x[", which_min_ess, "]")
mcmc_hist_r_scale(samp_jags_nom[, , xmin])
Result: Jags seems to be able to sample from the nominal parameterization of the Cauchy distribution just fine.
Appendix D: A centered eight schools model with very long chains and thinning
We continue with our discussion about hierarchical models on the Eight Schools data, which we started in Section Eight Schools. We also analyse the performance of different variants of the diagnostics.
A Centered Eight Schools model
writeLines(readLines("eight_schools_cp.stan"))data {
int<lower=0> J;
real y[J];
real<lower=0> sigma[J];
}
parameters {
real mu;
real<lower=0> tau;
real theta[J];
}
model {
mu ~ normal(0, 5);
tau ~ cauchy(0, 5);
theta ~ normal(mu, tau);
y ~ normal(theta, sigma);
}In the main text, we observed that the centered parameterization of this hierarchical model did not work well with the default MCMC options of Stan plus increased adapt_delta, and so we directly try to fit the model with longer chains.
Centered parameterization with longer chains
Low efficiency can be sometimes compensated with longer chains. Let’s check 10 times longer chain.
fit_cp2 <- stan(
file = 'eight_schools_cp.stan', data = eight_schools,
iter = 20000, chains = 4, seed = 483892929, refresh = 0,
control = list(adapt_delta = 0.95)
)Warning: There were 736 divergent transitions after warmup. Increasing adapt_delta above 0.95 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmupWarning: There were 2 chains where the estimated Bayesian Fraction of Missing Information was low. See
http://mc-stan.org/misc/warnings.html#bfmi-lowWarning: Examine the pairs() plot to diagnose sampling problemsmonitor(fit_cp2)Inference for the input samples (4 chains: each with iter = 20000; warmup = 10000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -0.99 4.43 9.73 4.39 3.29 1.00 4802 10367
tau 0.49 2.95 10.04 3.81 3.22 1.01 832 366
theta[1] -1.43 5.69 16.40 6.31 5.68 1.00 6735 12099
theta[2] -2.43 4.85 12.71 4.93 4.72 1.00 8848 16195
theta[3] -5.02 4.15 11.97 3.90 5.37 1.00 8249 13861
theta[4] -2.72 4.75 12.65 4.79 4.82 1.00 7967 15686
theta[5] -4.45 3.83 10.65 3.56 4.68 1.00 7141 14542
theta[6] -4.17 4.20 11.73 4.04 4.92 1.00 8512 14927
theta[7] -0.81 5.92 15.58 6.44 5.15 1.00 5895 13519
theta[8] -3.40 4.80 13.57 4.86 5.43 1.00 8356 14080
lp__ -25.06 -15.17 -2.25 -14.64 6.82 1.01 849 491
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(fit_cp2)
print(res)Inference for the input samples (4 chains: each with iter = 20000; warmup = 10000):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat sRhat
mu 4.39 0.05 3.29 -0.99 4.43 9.73 4757 0.12 4808 4752 4802 0.12 1 1.00
tau 3.81 0.07 3.22 0.49 2.95 10.04 2037 0.05 2049 813 832 0.02 1 1.00
theta[1] 6.31 0.07 5.68 -1.43 5.69 16.40 7278 0.18 7292 6706 6735 0.17 1 1.00
theta[2] 4.93 0.05 4.72 -2.43 4.85 12.71 9687 0.24 9788 8531 8848 0.22 1 1.00
theta[3] 3.90 0.05 5.37 -5.02 4.15 11.97 9992 0.25 10062 8107 8249 0.21 1 1.00
theta[4] 4.79 0.05 4.82 -2.72 4.75 12.65 9072 0.23 9194 7989 7967 0.20 1 1.00
theta[5] 3.56 0.05 4.68 -4.45 3.83 10.65 8243 0.21 8217 7117 7141 0.18 1 1.00
theta[6] 4.04 0.05 4.92 -4.17 4.20 11.73 9597 0.24 9573 8412 8512 0.21 1 1.00
theta[7] 6.44 0.07 5.15 -0.81 5.92 15.58 6263 0.16 6345 5802 5895 0.15 1 1.00
theta[8] 4.86 0.05 5.43 -3.40 4.80 13.57 10220 0.26 10312 8301 8356 0.21 1 1.00
lp__ -14.64 0.25 6.82 -25.06 -15.17 -2.25 768 0.02 787 826 849 0.02 1 1.01
zRhat zsRhat zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff madsseff madsreff
mu 1 1.00 1 8927 0.22 10367 0.26 4544 0.11 7935 0.20
tau 1 1.01 1 7095 0.18 366 0.01 2258 0.06 6209 0.16
theta[1] 1 1.00 1 4159 0.10 12099 0.30 5832 0.15 7815 0.20
theta[2] 1 1.00 1 7250 0.18 16195 0.40 6006 0.15 7676 0.19
theta[3] 1 1.00 1 8040 0.20 13861 0.35 4841 0.12 7523 0.19
theta[4] 1 1.00 1 7715 0.19 15686 0.39 4902 0.12 7975 0.20
theta[5] 1 1.00 1 9201 0.23 14542 0.36 4300 0.11 8048 0.20
theta[6] 1 1.00 1 9238 0.23 14927 0.37 5131 0.13 7877 0.20
theta[7] 1 1.00 1 6526 0.16 13519 0.34 5464 0.14 7855 0.20
theta[8] 1 1.00 1 6593 0.16 14080 0.35 5677 0.14 7474 0.19
lp__ 1 1.01 1 1489 0.04 491 0.01 2087 0.05 4927 0.12We still get a whole bunch of divergent transitions so it’s clear that the results can’t be trusted even if all other diagnostics were good. Still, it may be worth looking at additional diagnostics to better understand what’s happening.
Some rank-normalized split-Rhats are still larger than \(1.01\). Bulk-ESS for tau and lp__ are around 800 which corresponds to low relative efficiency of \(1\%\), but is above our recommendation of ESS>400. In this kind of cases, it is useful to look at the local efficiency estimates, too (and the larger number of divergences is clear indication of problems, too).
We examine the sampling efficiency in different parts of the posterior by computing the effective sample size for small intervals for tau.
plot_local_ess(fit = fit_cp2, par = "tau", nalpha = 50)
We see that the sampling has difficulties in exploring small tau values. As ESS<400 for small probability intervals in case of small tau values, we may suspect that we may miss substantial amount of posterior mass and get biased estimates.
We also examine the effective sample size of different quantile estimates.
plot_quantile_ess(fit = fit_cp2, par = "tau", nalpha = 100)
Several quantile estimates have ESS<400, which raises a doubt that there are convergence problems and we may have significant bias.
Let’s see how the Bulk-ESS and Tail-ESS changes when we use more and more draws.
plot_change_ess(fit = fit_cp2, par = "tau")
We see that given recommendation that Bulk-ESS>400 and Tail-ESS>400, they are not sufficient to detect convergence problems in this case, even the tail quantile estimates are able to detect these problems.
The rank plot visualisation of tau also shows clear sticking and mixing problems.
samp_cp2 <- as.array(fit_cp2)
mcmc_hist_r_scale(samp_cp2[, , "tau"])
Similar results are obtained for lp__, which is closely connected to tau for this model.
mcmc_hist_r_scale(samp_cp2[, , "lp__"])
We may also examine small interval efficiencies for mu.
plot_local_ess(fit = fit_cp2, par = "mu", nalpha = 50)
There are gaps of poor efficiency which again indicates problems in the mixing of the chains. However, these problems do not occur for any specific range of values of mu as was the case for tau. This tells us that it’s probably not mu with which the sampler has problems, but more likely tau or a related quantity.
As we observed divergences, we shouldn’t trust any Monte Carlo standard error (MCSE) estimates as they are likely biased, as well. However, for illustration purposes, we compute the MCSE, tail quantiles and corresponding effective sample sizes for the median of mu and tau. Comparing to the shorter MCMC run, using 10 times more draws has not reduced the MCSE to one third as would be expected without problems in the mixing of the chains.
round(mcse_quantile(samp_cp2[ , , "mu"], prob = 0.5), 2)[1] 0.06round(mcse_quantile(samp_cp2[ , , "tau"], prob = 0.5), 2)[1] 0.07Centered parameterization with very long chains
For further evidence, let’s check 100 times longer chains than the default. This is not something we would recommend doing in practice, as it is not able to solve any problems with divergences as illustrated below.
fit_cp3 <- stan(
file = 'eight_schools_cp.stan', data = eight_schools,
iter = 200000, chains = 4, seed = 483892929, refresh = 0,
control = list(adapt_delta = 0.95)
)Warning: There were 9955 divergent transitions after warmup. Increasing adapt_delta above 0.95 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmupWarning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See
http://mc-stan.org/misc/warnings.html#bfmi-lowWarning: Examine the pairs() plot to diagnose sampling problemsmonitor(fit_cp3)Inference for the input samples (4 chains: each with iter = 2e+05; warmup = 1e+05):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.11 4.41 10.11 4.45 3.44 1 3928 1375
tau 0.44 2.91 10.03 3.77 3.21 1 1880 426
theta[1] -1.57 5.78 16.33 6.36 5.70 1 10822 92205
theta[2] -2.54 4.90 13.21 5.02 4.81 1 8299 1524
theta[3] -5.00 4.16 12.35 3.97 5.42 1 7603 1503
theta[4] -2.99 4.73 13.08 4.81 4.92 1 9006 1515
theta[5] -4.55 3.83 11.14 3.63 4.81 1 6077 1477
theta[6] -4.19 4.19 12.00 4.07 4.98 1 6986 1478
theta[7] -1.04 5.96 15.62 6.45 5.20 1 9461 74202
theta[8] -3.47 4.80 13.58 4.92 5.44 1 10828 78184
lp__ -24.94 -15.07 -2.28 -14.54 6.82 1 5161 2755
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(fit_cp3)
print(res)Inference for the input samples (4 chains: each with iter = 2e+05; warmup = 1e+05):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat sRhat
mu 4.45 0.06 3.44 -1.11 4.41 10.11 3393 0.01 3372 3954 3928 0.01 1 1
tau 3.77 0.03 3.21 0.44 2.91 10.03 11689 0.03 11680 1944 1880 0.00 1 1
theta[1] 6.36 0.05 5.70 -1.57 5.78 16.33 13139 0.03 12991 10915 10822 0.03 1 1
theta[2] 5.02 0.05 4.81 -2.54 4.90 13.21 8261 0.02 8184 8382 8299 0.02 1 1
theta[3] 3.97 0.06 5.42 -5.00 4.16 12.35 8482 0.02 8388 7660 7603 0.02 1 1
theta[4] 4.81 0.05 4.92 -2.99 4.73 13.08 9245 0.02 9211 9037 9006 0.02 1 1
theta[5] 3.63 0.06 4.81 -4.55 3.83 11.14 6536 0.02 6499 6115 6077 0.02 1 1
theta[6] 4.07 0.06 4.98 -4.19 4.19 12.00 7364 0.02 7301 7050 6986 0.02 1 1
theta[7] 6.45 0.05 5.20 -1.04 5.96 15.62 9899 0.02 9841 9514 9461 0.02 1 1
theta[8] 4.92 0.05 5.44 -3.47 4.80 13.58 12331 0.03 12286 10877 10828 0.03 1 1
lp__ -14.54 0.10 6.82 -24.94 -15.07 -2.28 4944 0.01 4940 5160 5161 0.01 1 1
zRhat zsRhat zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff madsseff madsreff
mu 1 1 1 3198 0.01 1375 0.00 18404 0.05 14646 0.04
tau 1 1 1 36678 0.09 426 0.00 14021 0.04 15074 0.04
theta[1] 1 1 1 17126 0.04 92205 0.23 17781 0.04 15456 0.04
theta[2] 1 1 1 10430 0.03 1524 0.00 17680 0.04 14336 0.04
theta[3] 1 1 1 11295 0.03 1503 0.00 19052 0.05 13816 0.03
theta[4] 1 1 1 11909 0.03 1515 0.00 18180 0.05 14777 0.04
theta[5] 1 1 1 8425 0.02 1477 0.00 18820 0.05 14004 0.04
theta[6] 1 1 1 10034 0.03 1478 0.00 17844 0.04 13732 0.03
theta[7] 1 1 1 12363 0.03 74202 0.19 17559 0.04 16240 0.04
theta[8] 1 1 1 16784 0.04 78184 0.20 18178 0.05 15473 0.04
lp__ 1 1 1 6886 0.02 2755 0.01 13314 0.03 16069 0.04Rhat, Bulk-ESS and Tail-ESS are not able to detect problems, although Tail-ESS for tau is suspiciously low compared to total number of draws.
plot_local_ess(fit = fit_cp3, par = "tau", nalpha = 100)
plot_quantile_ess(fit = fit_cp3, par = "tau", nalpha = 100)
And the rank plots of tau also show sticking and mixing problems for small values of tau.
samp_cp3 <- as.array(fit_cp3)
mcmc_hist_r_scale(samp_cp3[, , "tau"])
What we do see is an advantage of rank plots over trace plots as even with 100000 draws per chain, rank plots don’t get crowded and the mixing problems of chains is still easy to see.
With centered parameterization the mean estimate tends to get smaller with more draws. With 400000 draws using the centered parameterization the mean estimate is 3.77 (se 0.03). With 40000 draws using the non-centered parameterization the mean estimate is 3.6 (se 0.02). The difference is more than 8 sigmas. We are able to see the convergence problems in the centered parameterization case, if we do look carefully (or use divergence diagnostic ), but we do see that Rhat, Bulk-ESS, Tail-ESS and Monte Carlo error estimates for the mean can’t be trusted if other diagnostics indicate convergence problems!
Centered parameterization with very long chains and thinning
When autocorrelation time is high, it has been common to thin the chains by saving only a small portion of the draws. This will throw away useful information also for convergence diagnostics. With 400000 iterations per chain, thinning of 200 and 4 chains, we again end up with 4000 iterations as with the default settings.
fit_cp4 <- stan(
file = 'eight_schools_cp.stan', data = eight_schools,
iter = 400000, thin = 200, chains = 4, seed = 483892929, refresh = 0,
control = list(adapt_delta = 0.95)
)Warning: There were 74 divergent transitions after warmup. Increasing adapt_delta above 0.95 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmupWarning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See
http://mc-stan.org/misc/warnings.html#bfmi-lowWarning: Examine the pairs() plot to diagnose sampling problemsWe observe several divergent transitions and the estimated Bayesian fraction of missing information is also low, which indicate convergence problems and potentially biased estimates.
Unfortunately the thinning makes Rhat and ESS estimates to miss the problems. The posterior mean is still biased, being more than 3 sigmas away from the estimate obtained using non-centered parameterization.
monitor(fit_cp4)Inference for the input samples (4 chains: each with iter = 4e+05; warmup = 2e+05):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.15 4.29 9.84 4.30 3.33 1 3992 3773
tau 0.43 2.93 9.75 3.70 3.12 1 2572 1718
theta[1] -1.74 5.43 16.44 6.04 5.55 1 4091 3875
theta[2] -2.25 4.73 13.00 4.97 4.76 1 4064 3648
theta[3] -4.96 4.10 12.11 3.85 5.34 1 4138 3973
theta[4] -3.12 4.58 12.54 4.68 4.80 1 4055 3868
theta[5] -4.51 3.68 10.70 3.47 4.72 1 4062 3514
theta[6] -4.19 4.15 11.75 3.95 4.94 1 3969 3788
theta[7] -1.22 5.75 15.44 6.23 5.08 1 3882 3536
theta[8] -3.91 4.68 13.54 4.80 5.40 1 3973 3814
lp__ -24.91 -15.15 -1.17 -14.29 7.11 1 2676 1765
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(fit_cp4)
print(res)Inference for the input samples (4 chains: each with iter = 4e+05; warmup = 2e+05):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat sRhat
mu 4.30 0.05 3.33 -1.15 4.29 9.84 3972 0.99 3990 3974 3992 1.00 1 1
tau 3.70 0.05 3.12 0.43 2.93 9.75 3339 0.83 3398 2567 2572 0.64 1 1
theta[1] 6.04 0.09 5.55 -1.74 5.43 16.44 4027 1.01 4036 4081 4091 1.02 1 1
theta[2] 4.97 0.07 4.76 -2.25 4.73 13.00 4043 1.01 4055 4055 4064 1.02 1 1
theta[3] 3.85 0.08 5.34 -4.96 4.10 12.11 4188 1.05 4205 4121 4138 1.03 1 1
theta[4] 4.68 0.08 4.80 -3.12 4.58 12.54 4013 1.00 4048 4020 4055 1.01 1 1
theta[5] 3.47 0.07 4.72 -4.51 3.68 10.70 3990 1.00 4022 4003 4062 1.02 1 1
theta[6] 3.95 0.08 4.94 -4.19 4.15 11.75 3729 0.93 3804 3928 3969 0.99 1 1
theta[7] 6.23 0.08 5.08 -1.22 5.75 15.44 3891 0.97 3899 3874 3882 0.97 1 1
theta[8] 4.80 0.09 5.40 -3.91 4.68 13.54 3987 1.00 4019 3942 3973 0.99 1 1
lp__ -14.29 0.14 7.11 -24.91 -15.15 -1.17 2566 0.64 2571 2667 2676 0.67 1 1
zRhat zsRhat zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff madsseff madsreff
mu 1 1 1 4029 1.01 3773 0.94 4046 1.01 3762 0.94
tau 1 1 1 3818 0.95 1718 0.43 3755 0.94 4101 1.03
theta[1] 1 1 1 3696 0.92 3875 0.97 4130 1.03 3987 1.00
theta[2] 1 1 1 3938 0.98 3648 0.91 4036 1.01 3583 0.90
theta[3] 1 1 1 4033 1.01 3973 0.99 3656 0.91 3853 0.96
theta[4] 1 1 1 3822 0.96 3868 0.97 3817 0.95 3539 0.88
theta[5] 1 1 1 3696 0.92 3514 0.88 3809 0.95 3492 0.87
theta[6] 1 1 1 3726 0.93 3788 0.95 3881 0.97 3709 0.93
theta[7] 1 1 1 3285 0.82 3536 0.88 3946 0.99 3927 0.98
theta[8] 1 1 1 3718 0.93 3814 0.95 3842 0.96 3765 0.94
lp__ 1 1 1 2932 0.73 1765 0.44 3682 0.92 3788 0.95Various diagnostic plots of tau look reasonable as well.
plot_local_ess(fit = fit_cp4, par = "tau", nalpha = 100)
plot_quantile_ess(fit = fit_cp4, par = "tau", nalpha = 100)
plot_change_ess(fit = fit_cp4, par = "tau")
However, the rank plots seem still to show the problem.
samp_cp4 <- as.array(fit_cp4)
mcmc_hist_r_scale(samp_cp4[, , "tau"])
Non-centered Eight Schools model
In the following, we want to expand our understanding of the non-centered parameterization of the hierarchical model fit to the eight schools data.
writeLines(readLines("eight_schools_ncp.stan"))data {
int<lower=0> J;
real y[J];
real<lower=0> sigma[J];
}
parameters {
real mu;
real<lower=0> tau;
real theta_tilde[J];
}
transformed parameters {
real theta[J];
for (j in 1:J)
theta[j] = mu + tau * theta_tilde[j];
}
model {
mu ~ normal(0, 5);
tau ~ cauchy(0, 5);
theta_tilde ~ normal(0, 1);
y ~ normal(theta, sigma);
}Non-centered parameterization with default MCMC options
In the main text, we have already seen that the non-centered parameterization works better than the centered parameterization, at least when we use an increased adapt_delta value. Let’s see what happens when using the default MCMC option of Stan.
fit_ncp <- stan(
file = 'eight_schools_ncp.stan', data = eight_schools,
iter = 2000, chains = 4, seed = 483892929, refresh = 0
)We observe a few divergent transitions with the default of adapt_delta=0.8. Let’s analyze the sample.
monitor(fit_ncp)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.13 4.49 9.74 4.41 3.33 1.00 4639 2369
tau 0.27 2.67 9.66 3.50 3.08 1.00 2564 1954
theta_tilde[1] -1.38 0.33 1.96 0.31 1.01 1.00 5248 2906
theta_tilde[2] -1.47 0.11 1.63 0.09 0.94 1.00 4925 3130
theta_tilde[3] -1.67 -0.09 1.54 -0.07 0.98 1.00 5737 2902
theta_tilde[4] -1.49 0.03 1.59 0.04 0.94 1.00 4844 3066
theta_tilde[5] -1.65 -0.17 1.38 -0.16 0.92 1.01 4356 2927
theta_tilde[6] -1.64 -0.11 1.45 -0.09 0.94 1.00 4041 2537
theta_tilde[7] -1.25 0.35 1.87 0.33 0.96 1.00 4678 2839
theta_tilde[8] -1.50 0.08 1.63 0.08 0.98 1.00 5083 2651
theta[1] -1.54 5.49 15.80 6.12 5.53 1.00 4530 3126
theta[2] -2.69 4.83 12.60 4.89 4.67 1.00 5345 3072
theta[3] -4.50 4.18 11.89 4.00 5.15 1.00 4466 2890
theta[4] -2.82 4.61 12.21 4.69 4.73 1.00 4211 2883
theta[5] -4.03 3.96 10.70 3.70 4.57 1.00 4656 3186
theta[6] -4.12 4.17 11.45 3.99 4.75 1.00 5097 3158
theta[7] -0.83 5.80 15.09 6.26 5.04 1.00 4019 3044
theta[8] -3.15 4.81 13.49 4.87 5.21 1.00 4593 2947
lp__ -11.33 -6.65 -3.77 -6.97 2.33 1.00 1525 2499
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(fit_ncp)
print(res)Inference for the input samples (4 chains: each with iter = 2000; warmup = 1000):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat
mu 4.41 0.05 3.33 -1.13 4.49 9.74 4539 1.13 4602 4575 4639 1.16 1
tau 3.50 0.06 3.08 0.27 2.67 9.66 2858 0.71 2881 2531 2564 0.64 1
theta_tilde[1] 0.31 0.01 1.01 -1.38 0.33 1.96 5232 1.31 5250 5230 5248 1.31 1
theta_tilde[2] 0.09 0.01 0.94 -1.47 0.11 1.63 4829 1.21 4871 4883 4925 1.23 1
theta_tilde[3] -0.07 0.01 0.98 -1.67 -0.09 1.54 5686 1.42 5750 5672 5737 1.43 1
theta_tilde[4] 0.04 0.01 0.94 -1.49 0.03 1.59 4793 1.20 4844 4792 4844 1.21 1
theta_tilde[5] -0.16 0.01 0.92 -1.65 -0.17 1.38 4287 1.07 4358 4286 4356 1.09 1
theta_tilde[6] -0.09 0.01 0.94 -1.64 -0.11 1.45 3999 1.00 4035 4005 4041 1.01 1
theta_tilde[7] 0.33 0.01 0.96 -1.25 0.35 1.87 4599 1.15 4682 4596 4678 1.17 1
theta_tilde[8] 0.08 0.01 0.98 -1.50 0.08 1.63 5046 1.26 5084 5047 5083 1.27 1
theta[1] 6.12 0.09 5.53 -1.54 5.49 15.80 4204 1.05 4236 4486 4530 1.13 1
theta[2] 4.89 0.07 4.67 -2.69 4.83 12.60 5078 1.27 5095 5327 5345 1.34 1
theta[3] 4.00 0.08 5.15 -4.50 4.18 11.89 4064 1.02 4142 4375 4466 1.12 1
theta[4] 4.69 0.08 4.73 -2.82 4.61 12.21 3852 0.96 3976 4065 4211 1.05 1
theta[5] 3.70 0.07 4.57 -4.03 3.96 10.70 4372 1.09 4471 4555 4656 1.16 1
theta[6] 3.99 0.07 4.75 -4.12 4.17 11.45 4837 1.21 4850 5083 5097 1.27 1
theta[7] 6.26 0.08 5.04 -0.83 5.80 15.09 3924 0.98 3954 3988 4019 1.00 1
theta[8] 4.87 0.08 5.21 -3.15 4.81 13.49 4535 1.13 4534 4577 4593 1.15 1
lp__ -6.97 0.06 2.33 -11.33 -6.65 -3.77 1426 0.36 1466 1483 1525 0.38 1
sRhat zRhat zsRhat zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff
mu 1 1 1 1.00 2019 0.50 2369 0.59 4857 1.21
tau 1 1 1 1.00 2979 0.74 1954 0.49 3344 0.84
theta_tilde[1] 1 1 1 1.00 1982 0.50 2906 0.73 4882 1.22
theta_tilde[2] 1 1 1 1.00 2044 0.51 3130 0.78 4903 1.23
theta_tilde[3] 1 1 1 1.00 1874 0.47 2902 0.73 5213 1.30
theta_tilde[4] 1 1 1 1.00 2169 0.54 3066 0.77 4657 1.16
theta_tilde[5] 1 1 1 1.01 2070 0.52 2927 0.73 3907 0.98
theta_tilde[6] 1 1 1 1.00 2004 0.50 2537 0.63 4378 1.09
theta_tilde[7] 1 1 1 1.00 1973 0.49 2839 0.71 4774 1.19
theta_tilde[8] 1 1 1 1.00 1817 0.45 2651 0.66 4750 1.19
theta[1] 1 1 1 1.00 2614 0.65 3126 0.78 4467 1.12
theta[2] 1 1 1 1.00 2321 0.58 3072 0.77 4929 1.23
theta[3] 1 1 1 1.00 2477 0.62 2890 0.72 5071 1.27
theta[4] 1 1 1 1.00 2277 0.57 2883 0.72 4857 1.21
theta[5] 1 1 1 1.00 2253 0.56 3186 0.80 4928 1.23
theta[6] 1 1 1 1.00 2242 0.56 3158 0.79 4967 1.24
theta[7] 1 1 1 1.00 2553 0.64 3044 0.76 4544 1.14
theta[8] 1 1 1 1.00 2228 0.56 2947 0.74 4553 1.14
lp__ 1 1 1 1.00 2458 0.61 2499 0.62 2110 0.53
madsseff madsreff
mu 2146 0.54
tau 2938 0.73
theta_tilde[1] 2295 0.57
theta_tilde[2] 2127 0.53
theta_tilde[3] 2232 0.56
theta_tilde[4] 2466 0.62
theta_tilde[5] 2203 0.55
theta_tilde[6] 2533 0.63
theta_tilde[7] 2525 0.63
theta_tilde[8] 2196 0.55
theta[1] 2697 0.67
theta[2] 2644 0.66
theta[3] 2628 0.66
theta[4] 2552 0.64
theta[5] 2599 0.65
theta[6] 2418 0.60
theta[7] 2831 0.71
theta[8] 2590 0.65
lp__ 3016 0.75All Rhats are close to 1, and ESSs are good despite a few divergent transitions. Small interval and quantile plots of tau reveal some sampling problems for small tau values, but not nearly as strong as for the centered parameterization.
plot_local_ess(fit = fit_ncp, par = "tau", nalpha = 20)
plot_quantile_ess(fit = fit_ncp, par = "tau", nalpha = 40)
Overall, the non-centered parameterization looks good even for the default settings of adapt_delta, and increasing it to 0.95 gets rid of the last remaining problems. This stands in sharp contrast to what we observed for the centered parameterization, where increasing adapt_delta didn’t help at all. Actually, this is something we observe quite often: A suboptimal parameterization can cause problems that are not simply solved by tuning the sampler. Instead, we have to adjust our model to achieve trustworthy inference.
Appendix E: A centered eight schools model fit using a Gibbs sampler
We will also run the centered and non-centered parameterizations of the eight schools model with Jags.
Centered Eight Schools Model
The Jags code for the centered eight schools model looks as follows:
writeLines(readLines("eight_schools_cp.bugs"))model {
for (j in 1:J) {
sigma_prec[j] <- pow(sigma[j], -2)
theta[j] ~ dnorm(mu, tau_prec)
y[j] ~ dnorm(theta[j], sigma_prec[j])
}
mu ~ dnorm(0, pow(5, -2))
tau ~ dt(0, pow(5, -2), 1)T(0, )
tau_prec <- pow(tau, -2)
}First, we initialize the Jags model for reusage later.
jags_cp <- jags.model(
"eight_schools_cp.bugs",
data = eight_schools,
n.chains = 4, n.adapt = 10000
)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 40
Initializing modelNext, we sample 1000 iterations for each of the 4 chains for easy comparison with the corresponding Stan results.
samp_jags_cp <- coda.samples(
jags_cp, c("theta", "mu", "tau"),
n.iter = 1000
)
samp_jags_cp <- aperm(abind(samp_jags_cp, along = 3), c(1, 3, 2))Convergence diagnostics indicate problems in the sampling of mu and tau, but also to a lesser degree in all other paramters.
mon <- monitor(samp_jags_cp)
print(mon)Inference for the input samples (4 chains: each with iter = 1000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.24 4.40 9.84 4.40 3.34 1.04 136 227
tau 0.36 3.16 8.91 3.70 2.71 1.04 116 147
theta[1] -1.45 5.79 16.13 6.27 5.55 1.01 314 1043
theta[2] -2.64 4.82 12.88 4.94 4.77 1.01 321 1021
theta[3] -4.76 4.12 12.20 3.95 5.16 1.02 316 634
theta[4] -2.99 4.68 12.97 4.78 4.87 1.02 287 846
theta[5] -4.53 3.71 11.13 3.59 4.71 1.02 232 370
theta[6] -4.08 4.08 11.89 4.03 4.87 1.02 270 561
theta[7] -1.08 5.81 15.23 6.28 5.02 1.01 356 1252
theta[8] -3.15 4.79 13.05 4.87 5.08 1.01 326 979
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).We also see problems in the sampling of tau using various diagnostic plots.
plot_local_ess(samp_jags_cp, par = "tau", nalpha = 20)
plot_quantile_ess(samp_jags_cp, par = "tau", nalpha = 20)
plot_change_ess(samp_jags_cp, par = "tau")
Let’s see what happens if we run 10 times longer chains.
samp_jags_cp <- coda.samples(
jags_cp, c("theta", "mu", "tau"),
n.iter = 10000
)
samp_jags_cp <- aperm(abind(samp_jags_cp, along = 3), c(1, 3, 2))Convergence looks better now, although tau is still estimated not very efficiently.
mon <- monitor(samp_jags_cp)
print(mon)Inference for the input samples (4 chains: each with iter = 10000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.03 4.44 9.78 4.43 3.30 1 1312 2467
tau 0.27 2.83 9.71 3.62 3.18 1 820 716
theta[1] -1.49 5.76 16.15 6.29 5.56 1 2244 3428
theta[2] -2.50 4.93 12.67 4.97 4.68 1 2445 4689
theta[3] -4.82 4.24 11.73 3.97 5.24 1 2632 6113
theta[4] -2.79 4.79 12.45 4.80 4.77 1 2580 6898
theta[5] -4.42 3.88 10.71 3.63 4.68 1 2193 4575
theta[6] -3.94 4.25 11.51 4.08 4.83 1 2386 5430
theta[7] -1.02 5.93 15.33 6.34 5.06 1 2081 2934
theta[8] -3.26 4.85 13.32 4.91 5.23 1 2460 7930
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).The diagnostic plots of quantiles and small intervals tell a similar story.
plot_local_ess(samp_jags_cp, par = "tau", nalpha = 20)
plot_quantile_ess(samp_jags_cp, par = "tau", nalpha = 20)
Notably, however, the increase in effective sample size of tau is linear in the total number of draws indicating that convergence for tau may be achieved by simply running longer chains.
plot_change_ess(samp_jags_cp, par = "tau")
Result: Similar to Stan, Jags also has convergence problems with the centered parameterization of the eight schools model.
Non-Centered Eight Schools Model
The Jags code for the non-centered eight schools model looks as follows:
writeLines(readLines("eight_schools_ncp.bugs"))model {
for (j in 1:J) {
sigma_prec[j] <- pow(sigma[j], -2)
theta_tilde[j] ~ dnorm(0, 1)
theta[j] = mu + tau * theta_tilde[j]
y[j] ~ dnorm(theta[j], sigma_prec[j])
}
mu ~ dnorm(0, pow(5, -2))
tau ~ dt(0, pow(5, -2), 1)T(0, )
}First, we initialize the Jags model for reusage later.
jags_ncp <- jags.model(
"eight_schools_ncp.bugs",
data = eight_schools,
n.chains = 4, n.adapt = 10000
)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 55
Initializing modelNext, we sample 1000 iterations for each of the 4 chains for easy comparison with the corresponding Stan results.
samp_jags_ncp <- coda.samples(
jags_ncp, c("theta", "mu", "tau"),
n.iter = 1000
)
samp_jags_ncp <- aperm(abind(samp_jags_ncp, along = 3), c(1, 3, 2))Convergence diagnostics indicate much better mixing than for the centered eight school model.
mon <- monitor(samp_jags_ncp)
print(mon)Inference for the input samples (4 chains: each with iter = 1000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
mu -1.03 4.39 9.80 4.38 3.31 1 3253 3252
tau 0.24 2.82 9.87 3.66 3.21 1 1252 1395
theta[1] -1.56 5.79 16.91 6.33 5.73 1 3033 2408
theta[2] -2.39 4.86 12.80 4.97 4.71 1 4026 3589
theta[3] -5.17 4.03 12.02 3.82 5.38 1 3543 2903
theta[4] -2.89 4.71 12.69 4.80 4.87 1 3902 3220
theta[5] -4.68 3.77 10.71 3.53 4.66 1 3743 3027
theta[6] -4.46 4.10 11.74 3.97 4.90 1 3729 3165
theta[7] -1.06 5.87 15.45 6.30 5.11 1 3177 3387
theta[8] -3.03 4.73 13.46 4.90 5.22 1 3796 3193
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).Specifically, the mixing of tau looks much better although we still see some problems in the estimation of larger quantiles.
plot_local_ess(samp_jags_ncp, par = "tau", nalpha = 20)
plot_quantile_ess(samp_jags_ncp, par = "tau", nalpha = 20)
Change in effective sample size is roughly linear indicating that some remaining convergence problems are likely to be solved by running longer chains.
plot_change_ess(samp_jags_ncp, par = "tau")
Result: Similar to Stan, Jags can sample from the non-centered parameterization of the eight schools model much better than from the centered parameterization.
Appendix F: Examples of rank normalization
We will illustrate the rank normalization with a few examples. First, we plot histograms, and empirical cumulative distribution functions (ECDF) with respect to the original parameter values (\(\theta\)), scaled ranks (ranks divided by the maximum rank), and rank normalized values (z). We used scaled ranks to make the plots look similar for different number of draws.
100 draws from Normal(0, 1):
n <- 100
theta <- rnorm(n)
plot_ranknorm(theta, n)
100 draws from Exponential(1):
theta <- rexp(n)
plot_ranknorm(theta, n)
100 draws from Cauchy(0, 1):
theta <- rcauchy(n)
plot_ranknorm(theta, n)
In the above plots, the ECDF with respect to scaled rank and rank normalized \(z\)-values look exactly the same for all distributions. In Split-\(\widehat{R}\) and effective sample size computations, we rank all draws jointly, but then compare ranks and ECDF of individual split chains. To illustrate the variation between chains, we draw 8 batches of 100 draws each from Normal(0, 1):
n <- 100
m <- 8
theta <- rnorm(n * m)
plot_ranknorm(theta, n, m)
The variation in ECDF due to the variation ranks is now visible also in scaled ranks and rank normalized \(z\)-values from different batches (chains).
The benefit of rank normalization is more obvious for non-normal distribution such as Cauchy:
theta <- rcauchy(n * m)
plot_ranknorm(theta, n, m)
Rank normalization makes the subsequent computations well defined and invariant under bijective transformations. This means that we get the same results, for example, if we use unconstrained or constrained parameterisations in a model.
Appendix G: Variance of the cumulative distribution function
In the paper, we had defined the empirical CDF (ECDF) for any \(\theta_\alpha\) as \[ p(\theta \leq \theta_\alpha) \approx \bar{I}_\alpha = \frac{1}{S}\sum_{s=1}^S I(\theta^{(s)} \leq\theta_\alpha), \]
For independent draws, \(\bar{I}_\alpha\) has a \({\rm Beta}(S\bar{I}_\alpha+1, S - S\bar{I}_\alpha + 1)\) distribution. Thus we can easily examine the variation of the ECDF for any \(\theta_\alpha\) value from a single chain. If \(\bar{I}_\alpha\) is not very close to \(1\) or \(S\) and \(S\) is large, we can use the variance of Beta distribution
\[ {\rm Var}[p(\theta \leq \theta_\alpha)] = \frac{(S\bar{I}_\alpha+1)*(S-S\bar{I}_\alpha+1)}{(S+2)^2(S+3)}. \] We illustrate uncertainty intervals of the Beta distribution and normal approximation of ECDF for 100 draws from Normal(0, 1):
n <- 100
m <- 1
theta <- rnorm(n * m)
plot_ranknorm(theta, n, m, interval = TRUE)
Uncertainty intervals of ECDF for draws from Cauchy(0, 1) illustrate again the improved visual clarity in plotting when using scaled ranks:
n <- 100
m <- 1
theta <- rcauchy(n * m)
plot_ranknorm(theta, n, m, interval = TRUE)
The above plots illustrate that the normal approximation is accurate for practical purposes in MCMC diagnostics.
Appendix H: Dynamic HMC and effective sample size
We have already seen that the effective sample size of dynamic HMC can be higher than with independent draws. The next example illustrates interesting relative efficiency phenomena due to the properties of dynamic HMC algorithms.
We sample from a simple 16-dimensional standard normal model.
writeLines(readLines("normal.stan"))data {
int<lower=1> J;
}
parameters {
vector[J] x;
}
model {
x ~ normal(0, 1);
}fit_n <- stan(
file = 'normal.stan', data = data.frame(J = 16),
iter = 20000, chains = 4, seed = 483892929, refresh = 0
)samp <- as.array(fit_n)
monitor(samp)Inference for the input samples (4 chains: each with iter = 10000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x[1] -1.62 0.00 1.63 0.00 0.99 1 96964 28204
x[2] -1.64 0.01 1.66 0.00 1.00 1 104386 29463
x[3] -1.66 0.00 1.64 0.00 1.00 1 93292 28774
x[4] -1.65 0.00 1.66 0.00 1.01 1 101123 28276
x[5] -1.64 0.00 1.64 0.00 1.00 1 102641 26885
x[6] -1.65 0.01 1.64 0.00 1.00 1 93715 28760
x[7] -1.65 -0.01 1.63 -0.01 0.99 1 97582 29527
x[8] -1.63 0.00 1.64 0.00 1.00 1 99873 29681
x[9] -1.64 0.00 1.65 0.00 1.00 1 103197 29306
x[10] -1.63 0.00 1.63 0.00 0.99 1 99133 29955
x[11] -1.65 0.00 1.66 -0.01 1.00 1 99900 29394
x[12] -1.64 0.00 1.65 0.00 1.00 1 99186 30576
x[13] -1.65 0.01 1.66 0.01 1.00 1 102224 28035
x[14] -1.64 0.00 1.65 0.00 1.00 1 97492 30145
x[15] -1.66 0.00 1.66 0.00 1.01 1 99684 28746
x[16] -1.65 0.00 1.65 0.00 1.00 1 97770 29004
lp__ -13.17 -7.67 -3.95 -7.99 2.85 1 14199 20120
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(samp)
print(res)Inference for the input samples (4 chains: each with iter = 10000; warmup = 0):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat sRhat
x[1] 0.00 0.00 0.99 -1.62 0.00 1.63 96783 2.42 96985 96762 96964 2.42 1 1
x[2] 0.00 0.00 1.00 -1.64 0.01 1.66 104164 2.60 104330 104222 104386 2.61 1 1
x[3] 0.00 0.00 1.00 -1.66 0.00 1.64 92845 2.32 93360 92777 93292 2.33 1 1
x[4] 0.00 0.00 1.01 -1.65 0.00 1.66 100571 2.51 100913 100779 101123 2.53 1 1
x[5] 0.00 0.00 1.00 -1.64 0.00 1.64 101800 2.54 102596 101841 102641 2.57 1 1
x[6] 0.00 0.00 1.00 -1.65 0.01 1.64 92936 2.32 93694 92959 93715 2.34 1 1
x[7] -0.01 0.00 0.99 -1.65 -0.01 1.63 97384 2.43 97622 97343 97582 2.44 1 1
x[8] 0.00 0.00 1.00 -1.63 0.00 1.64 99729 2.49 99926 99676 99873 2.50 1 1
x[9] 0.00 0.00 1.00 -1.64 0.00 1.65 102670 2.57 103199 102669 103197 2.58 1 1
x[10] 0.00 0.00 0.99 -1.63 0.00 1.63 98803 2.47 99108 98829 99133 2.48 1 1
x[11] -0.01 0.00 1.00 -1.65 0.00 1.66 99445 2.49 99973 99373 99900 2.50 1 1
x[12] 0.00 0.00 1.00 -1.64 0.00 1.65 98772 2.47 99147 98811 99186 2.48 1 1
x[13] 0.01 0.00 1.00 -1.65 0.01 1.66 101971 2.55 102253 101943 102224 2.56 1 1
x[14] 0.00 0.00 1.00 -1.64 0.00 1.65 97042 2.43 97322 97213 97492 2.44 1 1
x[15] 0.00 0.00 1.01 -1.66 0.00 1.66 99572 2.49 99744 99513 99684 2.49 1 1
x[16] 0.00 0.00 1.00 -1.65 0.00 1.65 97487 2.44 97755 97502 97770 2.44 1 1
lp__ -7.99 0.02 2.85 -13.17 -7.67 -3.95 14441 0.36 14451 14190 14199 0.35 1 1
zRhat zsRhat zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff madsseff madsreff
x[1] 1 1 1 16243 0.41 28204 0.71 77876 1.95 19353 0.48
x[2] 1 1 1 15972 0.40 29463 0.74 80234 2.01 19614 0.49
x[3] 1 1 1 16247 0.41 28774 0.72 76074 1.90 18879 0.47
x[4] 1 1 1 16331 0.41 28276 0.71 80886 2.02 19050 0.48
x[5] 1 1 1 16347 0.41 26885 0.67 80280 2.01 19270 0.48
x[6] 1 1 1 16742 0.42 28760 0.72 78685 1.97 19109 0.48
x[7] 1 1 1 16176 0.40 29527 0.74 78862 1.97 19179 0.48
x[8] 1 1 1 16877 0.42 29681 0.74 81155 2.03 19301 0.48
x[9] 1 1 1 16683 0.42 29306 0.73 76052 1.90 19718 0.49
x[10] 1 1 1 16926 0.42 29955 0.75 77572 1.94 19485 0.49
x[11] 1 1 1 16372 0.41 29394 0.73 79398 1.98 18610 0.47
x[12] 1 1 1 16845 0.42 30576 0.76 77831 1.95 19361 0.48
x[13] 1 1 1 15944 0.40 28035 0.70 77568 1.94 19714 0.49
x[14] 1 1 1 17372 0.43 30145 0.75 78145 1.95 20009 0.50
x[15] 1 1 1 16775 0.42 28746 0.72 80337 2.01 19650 0.49
x[16] 1 1 1 16821 0.42 29004 0.73 78544 1.96 19656 0.49
lp__ 1 1 1 21112 0.53 20120 0.50 16377 0.41 23830 0.60The Bulk-ESS for all \(x\) is larger than 9.329210^{4}. However tail-ESS for all \(x\) is less than 3.057610^{4}. Further, bulk-ESS for lp__ is only 1.419910^{4}.
If we take a look at all the Stan examples in this notebook, we see that the bulk-ESS for lp__ is always below 0.5. This is because lp__ correlates strongly with the total energy in HMC, which is sampled using a random walk proposal once per iteration. Thus, it’s likely that lp__ has some random walk behavior, as well, leading to autocorrelation and a small relative efficiency. At the same time, adaptive HMC can create antithetic Markov chains which have negative auto-correlations at odd lags. This results in a bulk-ESS greater than S for some parameters.
Let’s check the effective sample size in different parts of the posterior by computing the effective sample size for small interval estimates for x[1].
plot_local_ess(fit_n, par = 1, nalpha = 100)
The effective sample size for probability estimate for a small interval is close to 1 with a slight drop in the tails. This is a good result, but far from the effective sample size for the bulk, mean, and median estimates. Let’s check the effective sample size for quantiles.
plot_quantile_ess(fit = fit_n, par = 1, nalpha = 100)
Central quantile estimates have higher effective sample size than tail quantile estimates.
The total energy of HMC should affect how far in the tails a chain in one iteration can go. Fat tails of the target have high energy, and thus only chains with high total energy can reach there. This will suggest that the random walk in total energy would cause random walk in the variance of \(x\). Let’s check the second moment of \(x\).
samp_x2 <- as.array(fit_n, pars = "x")^2
monitor(samp_x2)Inference for the input samples (4 chains: each with iter = 10000; warmup = 0):
Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
x[1] 0 0.45 3.81 0.99 1.41 1 16245 18662
x[2] 0 0.44 3.92 1.00 1.42 1 15987 18325
x[3] 0 0.45 3.89 1.00 1.42 1 16239 18505
x[4] 0 0.46 3.95 1.02 1.46 1 16337 18987
x[5] 0 0.44 3.80 0.99 1.41 1 16337 17512
x[6] 0 0.46 3.82 1.00 1.39 1 16744 19941
x[7] 0 0.44 3.82 0.99 1.39 1 16157 19373
x[8] 0 0.46 3.80 1.00 1.40 1 16882 19860
x[9] 0 0.45 3.84 1.00 1.42 1 16689 18849
x[10] 0 0.44 3.80 0.98 1.40 1 16901 18608
x[11] 0 0.47 3.86 1.01 1.40 1 16353 18634
x[12] 0 0.46 3.88 1.00 1.44 1 16851 18726
x[13] 0 0.46 3.84 1.01 1.41 1 16042 18406
x[14] 0 0.45 3.82 0.99 1.40 1 17379 19875
x[15] 0 0.45 3.90 1.01 1.44 1 16787 18290
x[16] 0 0.45 3.87 1.01 1.43 1 16795 19508
For each parameter, Bulk_ESS and Tail_ESS are crude measures of
effective sample size for bulk and tail quantities respectively (an ESS > 100
per chain is considered good), and Rhat is the potential scale reduction
factor on rank normalized split chains (at convergence, Rhat <= 1.01).res <- monitor_extra(samp_x2)
print(res)Inference for the input samples (4 chains: each with iter = 10000; warmup = 0):
mean se_mean sd Q5 Q50 Q95 seff reff sseff zseff zsseff zsreff Rhat sRhat zRhat zsRhat
x[1] 0.99 0.01 1.41 0 0.45 3.81 15005 0.38 15036 16221 16245 0.41 1 1 1 1
x[2] 1.00 0.01 1.42 0 0.44 3.92 14177 0.35 14215 15951 15987 0.40 1 1 1 1
x[3] 1.00 0.01 1.42 0 0.45 3.89 14955 0.37 14957 16230 16239 0.41 1 1 1 1
x[4] 1.02 0.01 1.46 0 0.46 3.95 14417 0.36 14429 16329 16337 0.41 1 1 1 1
x[5] 0.99 0.01 1.41 0 0.44 3.80 14115 0.35 14126 16322 16337 0.41 1 1 1 1
x[6] 1.00 0.01 1.39 0 0.46 3.82 15469 0.39 15482 16702 16744 0.42 1 1 1 1
x[7] 0.99 0.01 1.39 0 0.44 3.82 15274 0.38 15295 16148 16157 0.40 1 1 1 1
x[8] 1.00 0.01 1.40 0 0.46 3.80 15860 0.40 15858 16892 16882 0.42 1 1 1 1
x[9] 1.00 0.01 1.42 0 0.45 3.84 14858 0.37 14877 16671 16689 0.42 1 1 1 1
x[10] 0.98 0.01 1.40 0 0.44 3.80 15165 0.38 15168 16895 16901 0.42 1 1 1 1
x[11] 1.01 0.01 1.40 0 0.47 3.86 14768 0.37 14768 16364 16353 0.41 1 1 1 1
x[12] 1.00 0.01 1.44 0 0.46 3.88 15040 0.38 15050 16835 16851 0.42 1 1 1 1
x[13] 1.01 0.01 1.41 0 0.46 3.84 14283 0.36 14288 16008 16042 0.40 1 1 1 1
x[14] 0.99 0.01 1.40 0 0.45 3.82 15837 0.40 15842 17375 17379 0.43 1 1 1 1
x[15] 1.01 0.01 1.44 0 0.45 3.90 15042 0.38 15045 16784 16787 0.42 1 1 1 1
x[16] 1.01 0.01 1.43 0 0.45 3.87 15049 0.38 15056 16775 16795 0.42 1 1 1 1
zfsRhat zfsseff zfsreff tailseff tailreff medsseff medsreff madsseff madsreff
x[1] 1 19175 0.48 18662 0.47 19333 0.48 23734 0.59
x[2] 1 18866 0.47 18325 0.46 19650 0.49 23883 0.60
x[3] 1 19529 0.49 18505 0.46 18849 0.47 24927 0.62
x[4] 1 17981 0.45 18987 0.47 19050 0.48 23031 0.58
x[5] 1 18617 0.47 17512 0.44 19222 0.48 23703 0.59
x[6] 1 19526 0.49 19941 0.50 19141 0.48 24476 0.61
x[7] 1 19261 0.48 19373 0.48 19193 0.48 24179 0.60
x[8] 1 19614 0.49 19860 0.50 19296 0.48 24893 0.62
x[9] 1 19024 0.48 18849 0.47 19670 0.49 23813 0.60
x[10] 1 19806 0.50 18608 0.47 19517 0.49 24384 0.61
x[11] 1 18996 0.47 18634 0.47 18601 0.47 24288 0.61
x[12] 1 19357 0.48 18726 0.47 19379 0.48 25021 0.63
x[13] 1 19236 0.48 18406 0.46 19710 0.49 24292 0.61
x[14] 1 19919 0.50 19875 0.50 20019 0.50 24606 0.62
x[15] 1 19258 0.48 18290 0.46 19640 0.49 24955 0.62
x[16] 1 19582 0.49 19508 0.49 19721 0.49 24357 0.61The mean of the bulk-ESS for \(x_j^2\) is 1.65453110^{4}, which is quite close to the bulk-ESS for lp__. This is not that surprising as the potential energy in normal model is proportional to \(\sum_{j=1}^J x_j^2\).
Let’s check the effective sample size in different parts of the posterior by computing the effective sample size for small interval probability estimates for x[1]^2.
plot_local_ess(fit = samp_x2, par = 1, nalpha = 100)
The effective sample size is mostly a bit below 1, but for the right tail of \(x_1^2\) the effective sample size drops. This is likely due to only some iterations having high enough total energy to obtain draws from the high energy part of the tail. Let’s check the effective sample size for quantiles.
plot_quantile_ess(fit = samp_x2, par = 1, nalpha = 100)
We can see the correlation between lp__ and magnitude of x[1] in the following plot.
samp <- as.array(fit_n)
qplot(
as.vector(samp[, , "lp__"]),
abs(as.vector(samp[, , "x[1]"]))
) +
labs(x = 'lp__', y = 'x[1]')
Low lp__ values corresponds to high energy and more variation in x[1], and high lp__ corresponds to low energy and small variation in x[1]. Finally \(\sum_{j=1}^J x_j^2\) is perfectly correlated with lp__.
qplot(
as.vector(samp[, , "lp__"]),
as.vector(apply(samp[, , 1:16]^2, 1:2, sum))
) +
labs(x = 'lp__', y = 'sum(x^2)')
This shows that even if we get high effective sample size estimates for central quantities (like mean or median), it is important to look at the relative efficiency of scale and tail quantities, as well. The effective sample size of lp__ can also indicate problems of sampling in the tails.
Original Computing Environment
makevars <- file.path(Sys.getenv("HOME"), ".R/Makevars")
if (file.exists(makevars)) {
writeLines(readLines(makevars))
}CXXFLAGS=-O3 -mtune=native -march=native -Wno-unused-variable -Wno-unused-function
CXXFLAGS+=-flto -ffat-lto-objects -Wno-unused-local-typedefs
CXXFLAGS+=-std=c++11
CFLAGS+=-O3
MAKEFLAGS = -j4devtools::session_info("rstan")─ Session info ───────────────────────────────────────────────────────────────────────────────────
setting value
version R version 3.5.1 (2018-07-02)
os Ubuntu 16.04.6 LTS
system x86_64, linux-gnu
ui X11
language en_GB:en
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Helsinki
date 2019-07-05
─ Packages ───────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.5.1)
backports 1.1.4 2019-04-10 [1] CRAN (R 3.5.1)
BH 1.69.0-1 2019-01-07 [1] CRAN (R 3.5.1)
callr 3.2.0 2019-03-15 [1] CRAN (R 3.5.1)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.5.1)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.5.1)
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desc 1.2.0 2018-05-01 [1] CRAN (R 3.5.1)
digest 0.6.18 2018-10-10 [1] CRAN (R 3.5.1)
fansi 0.4.0 2018-10-05 [1] CRAN (R 3.5.1)
ggplot2 * 3.1.1 2019-04-07 [1] CRAN (R 3.5.1)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.5.1)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 3.5.1)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.5.1)
inline 0.3.15 2018-05-18 [1] CRAN (R 3.5.1)
labeling 0.3 2014-08-23 [1] CRAN (R 3.5.1)
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lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.5.1)
loo 2.1.0.9000 2019-05-14 [1] Github (stan-dev/loo@f847129)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.5.1)
MASS 7.3-51.4 2019-04-26 [1] CRAN (R 3.5.1)
Matrix 1.2-17 2019-03-22 [1] CRAN (R 3.5.1)
matrixStats 0.54.0 2018-07-23 [1] CRAN (R 3.5.1)
mgcv 1.8-28 2019-03-21 [1] CRAN (R 3.5.1)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.5.1)
nlme 3.1-140 2019-05-12 [1] CRAN (R 3.5.1)
pillar 1.4.0 2019-05-11 [1] CRAN (R 3.5.1)
pkgbuild 1.0.3 2019-03-20 [1] CRAN (R 3.5.1)
pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.5.1)
plyr 1.8.4 2016-06-08 [1] CRAN (R 3.5.1)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.1)
processx 3.3.1 2019-05-08 [1] CRAN (R 3.5.1)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.5.1)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.5.1)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.5.1)
Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.5.1)
RcppEigen 0.3.3.5.0 2018-11-24 [1] CRAN (R 3.5.1)
reshape2 1.4.3 2017-12-11 [1] CRAN (R 3.5.1)
rlang 0.3.4 2019-04-07 [1] CRAN (R 3.5.1)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.5.1)
rstan * 2.18.2 2018-11-07 [1] CRAN (R 3.5.1)
scales 1.0.0 2018-08-09 [1] CRAN (R 3.5.1)
StanHeaders * 2.18.1-10 2019-06-14 [1] CRAN (R 3.5.1)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.5.1)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.5.1)
tibble * 2.1.1 2019-03-16 [1] CRAN (R 3.5.1)
utf8 1.1.4 2018-05-24 [1] CRAN (R 3.5.1)
vctrs 0.1.0 2018-11-29 [1] CRAN (R 3.5.1)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.5.1)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.5.1)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.5.1)
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[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library