Aalto 2020 course will be completely online.
All the course material is available in a git repo (and these pages are for easier navigation). All the material can be used in other courses. Text and videos licensed under CC-BY-NC 4.0. Code licensed under BSD-3.
The material will be updated during the course. Exercise instructions and slides will be updated at latest on Monday of the corresponding week. The updated material will appear on web pages, or you can clone the repo and pull before checking new material. If you don’t want to learn git, you can download the latest zip file.
The electronic version of the course book Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin is available for non-commercial purposes. Hard copies are available from the publisher and many book stores. Aalto library has also copies. See also home page for the book, errata for the book, and chapter notes.
This course has been designed so that there is strong emphasis in computational aspects of Bayesian data analysis and using the latest computational tools.
If you find BDA3 too difficult to start with, I recommend
The course consists of 12 blocks in periods I and II. The blocks don’t match exactly specific weeks. For example, it’s good start reading the material for the next block while making the assignment for one block. There are 9 assignments and a project work with presentation, and thus the assignments are not in one-to-one correspondence with the blocks. The schedule below lists the blocks and how they connect to the topics, book chapters and assignments.
Currently the video links are for the videos recorded 2019. Part of the videos will be re-recorded.
Here is an overview of the schedule. Scroll down the page to see detailed instructions for each block. Remember that blocks are overlapping so that when you are working on assignment for one block, you should start watching videos and reading text for the next block.
|Block||Readings||Lectures||Assignment||Q&A Date||Assignment due date|
|1. Introduction||BDA3 Chapter 1||Computational probabilistic modeling,
Introduction to uncertainty and modelling,
Introduction to the course contents
|2. Basics of Bayesian inference||BDA3 Chapter 1,
BDA3 Chapter 2
Extra explanations 2,
|3. Multidimensional posterior||BDA3 Chapter 3||Lecture 3||Assignment 3,
|4. Monte Carlo||BDA3 Chapter 10||Lecture 4.1,
|5. Markov chain Monte Carlo||BDA3 Chapter 11||Lecture 5.1,
|6. Stan, HMC, PPL||BDA3 Chapter 12 + extra material on Stan||Lecture 6.1,
|7. Hierarchical models and exchangeability||BDA3 Chapter 5||Lecture 7.1,
|8. Model checking & cross-validation||BDA3 Chapter 6, BDA3 Chapter 7, Visualization in Bayesian workflow, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC||Lecture 8.1,
|Start project work||02.11.||N/A|
|9. Model comparison and selection||BDA3 Chapter 7 (not 7.2 and 7.3),
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
|10. Decision analysis||BDA3 Chapter 9||Lecture 10.1||Assignment 9,
|11. Normal approximation, frequency properties||BDA3 Chapter 4||Lecture 11.1,
|12. Extended topics||Optional: BDA3 Chapter 8,
BDA3 Chapter 14-18,
BDA3 Chapter 21
|13. Project evaluation||Project presentations: 14-18.12.||Evaluation week 51|
Course practicalities, material, assignments, project work, peergrading, QA sessions, TA sessions, prerequisites, chat, etc.
BDA3 Chapters 1+2, basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model.
Multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.
Numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.
Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.
HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, probabilistic programming and Stan. BDA3 Ch 12 + extra material
Hierarchical models and exchangeability. BDA3 Ch 5.
Model checking and cross-validation.
PSIS-LOO, K-fold-CV, model comparison and selection. Extra lecture on variable selection with projection predictive variable selection.
Decision analysis. BDA3 Ch 9.
Normal approximation (Laplace approximation), and large sample theory and counter examples. BDA3 Ch 4.
Frequency evaluation of Bayesian methods, hypothesis testing and variable selection. Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21.
We strongly recommend using R in the course as there are more packages for Stan and statistical analysis in R. If you are already fluent in Python, but not in R, then using Python may be easier, but it can still be more useful to learn also R. Unless you are already experienced and have figured out your preferred way to work with R, we recommend
Sanasta “bayesilainen” esiintyy Suomessa muutamaa erilaista kirjoitustapaa. Muoto “bayesilainen” on muodostettu yleisen vieraskielisten nimien taivutussääntöjen mukaan: “Jos nimi on kirjoitettuna takavokaalinen mutta äännettynä etuvokaalinen, kirjoitetaan päätteseen tavallisesti takavokaali etuvokaalin sijasta, esim. Birminghamissa, Thamesilla.” Terho Itkonen, Kieliopas, 6. painos, Kirjayhtymä, 1997.