Aki Vehtari – Master class in Bayesian statistics CIRM
Tuesday Stan practical class
Slides
Demos and case studies
- Stan mc-stan.org
- Andrew Gelman, Daniel Lee, and Jiqiang Guo (2015) Stan: A probabilistic programming language for Bayesian inference and optimization. preprint
- Carpenter et al (2017). Stan: A probabilistic programming language. Journal of Statistical Software 76(1). online
- Stan development team (2018). Modeling Language User’s Guide and Reference Manual. online, Part III has a large number of example models
- Basics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy Video 1, Video 2
- Michael Betancourt (2018). Scalable Bayesian Inference with Hamiltonian Monte Carlo. Video
- Michael Betancourt (2018). A Conceptual Introduction to Hamiltonian Monte Carlo. Online
- Cole C. Monnahan, James T. Thorson, and Trevor A. Branch (2016) Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Online
- Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman (2018). Visualization in Bayesian workflow. Journal of the Royal Statistical Society Series A, accepted for publication as discussion paper. Preprint
- Graphical posterior predictive checks using the bayesplot package. Vignette
- Workflow with prior and posterior predictive checking Case study
- StanCon talks and case studies
- rstan
- rstanarm
- brms
- Stan discussion forum
Friday Model assessment, comparison and selection lecture
Slides
Demos and case studies
References
- Bürkner, P.-C., Gabry, J., Vehtari, A. (2018). Leave-one-out
cross-validation for non-factorizable normal
models. arXiv:1810.10559
- Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding
predictive information criteria for Bayesian models. Statistics and
Computing, 24(6):997–1016.
Preprint
- Piironen, J., Paasiniemi, M., and Vehtari, A. (2018). Projective
Inference in High-dimensional Problems: Prediction and Feature
Selection. arXiv:1810.02406
- Piironen, J. and Vehtari, A. (2016), Comparison of Bayesian
predictive methods for model selection, Statistics and Computing
27(3), 711–735. Online
- Piironen, J., and Vehtari, A. (2017). On the hyperprior choice for
the global shrinkage parameter in the horseshoe prior. Proceedings
of the 20th International Conference on Artificial Intelligence and
Statistics, PMLR 54:905-913.
Online
- Piironen, J., and Vehtari, A. (2017). Sparsity information and
regularization in the horseshoe and other shrinkage priors. In
Electronic Journal of Statistics, 11(2):5018-5051.
Online
- Piironen, J., and Vehtari, A. (2018). Iterative supervised principal
components. Proceedings of the 21th International Conference on
Artificial Intelligence and Statistics, accepted for
publication.
arXiv preprint arXiv:1710.06229
- Vehtari, A., Gelman, A., Gabry, J. (2017). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. Statistics
and Computing. 27(5):1413–1432. arXiv
preprint.
- Vehtari, A., Gelman, A., Gabry, J. (2017). Pareto smoothed
importance sampling. arXiv
preprint.
- Vehtari, A., Mononen, T., Tolvanen, V., and Winther, O. (2016).
Bayesian leave-one-out cross-validation approximations for Gaussian
latent variable models. JMLR, 17(103):1–38.
Online
- Vehtari, A. and Ojanen, J.: 2012, A survey of Bayesian predictive
methods for model assessment, selection and comparison, Statistics
Surveys 6, 142–228. Online
- Williams, D. R., Piironen, J., Vehtari, A., and Rast,
P. (2018). Bayesian estimation of Gaussian graphical models with
projection predictive selection. arXiv:1801.05725
- Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2017). Using
stacking to average Bayesian predictive distributions. In Bayesian
Analysis, doi:10.1214/17-BA1091,
Online