This repository has course material for Bayesian Data Analysis course at Aalto (CS-E5710)
The material will be updated during the course. Exercise instructions and slides will be updated at latest on Monday of the corresponding week.
- Basic terms of probability theory
- probability, probability density, distribution
- sum, product rule, and Bayes' rule
- expectation, mean, variance, median
- in Finnish, see e.g. Stokastiikka ja tilastollinen ajattelu
- in English, see e.g. Wikipedia and Introduction to probability and statistics
- Some algebra and calculus
- Basic visualisation techniques (R or Python)
- histogram, density plot, scatter plot
- see e.g. BDA_R_demos
- see e.g. BDA_py_demos
If you find BDA3 too difficult to start with, I recommend
- For background prerequisties, see, e.g., chapters 2, 4 and 5 in Kruschke, "Doing Bayesian Data Analysis". Some of my students have found this useful.
- Richard McElreath's Statistical Rethinking book is easier and the latest videos of Statistical Rethinking: A Bayesian Course Using R and Stan are highly recommended even if you are following BDA3.
- Michael Betancourt has a different point of view in his introduction material, and many have found these also enlightening. Furthermore, his Hamiltonian Monte Carlo videos are highly recommended if you are taking this course.
Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Home page for the book.
- Background (Ch 1)
- Single-parameter models (Ch 2)
- Multiparameter models (Ch 3)
- Computational methods (Ch 10)
- Markov chain Monte Carlo (Ch 11--12)
- Extra material for Stan and probabilistic programming
- Hierarchical models (Ch 5)
- Model checking (Ch 6)
- Evaluating and comparing models (Ch 7)
- Decision analysis (Ch 9)
- Large sample properties and Laplace approximation (Ch 4)
- In addition you learn workflow for Bayesian data analysis
Video clips on selected topics are available in a Panopto folder. I've started recording these in February 2019, and more will appear in spring and fall 2019.
Exercises (67%) and a project work (33%). Minimum of 50% of points must be obtained from both the exercises and project work.
We recommend using R in the course as there are more packages for Stan in R. If you are already fluent in Python, but not in R, then using Python is probably easier. Unless you are already experienced and have figured out your preferred way to work with R, we recommend installing RStudio Desktop.
- Stan home page
- Introductory article in Journal of Statistical Software
- Documentation
- RStan installation
- PyStan installation
- Basics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy Part 1 and Part 2
- Dicing with the unknown
- Logic, Probability, and Bayesian Inference by Michael Betancourt
- Origin of word Bayesian
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.