This page, updated throughout the course, will feature slides from our sessions, and examples for you to try.

Session 0, Course outline

Session 1, Introductions, motivations for Bayes, overview of Bayesian statistics

Session 2, Review of probability, binomial sampling

Session 3, Binomial sampling continued. R notes [.Rmd, .pdf]

Session 4, Linear models. R script [.R]

Session 5, Multinomial sampling. R notes [.Rmd, .pdf]

Session 6, Model selection and model averaging. R script [.R]

Session 7, Generalized linear models. R notes [.Rmd, .pdf]

Session 8, Meta-analysis No R code for this session

Session 9, Testing and multiple testing [.Rmd , .pdf]

Session 10, Imputation, and software [.R, for DIC/WAIC examples]
Code illustrating various packages running the HWE example - each example is self-contained

• WinBUGS example - requires installing WinBUGS. If you don't remember where to point and click, watch the (unrated) WinBUGS the Movie
• R2WinBUGS and JAGS example - requires installing R and its R2WinBUGS package. For the JAGS part you will also need to install JAGS and the rjags package
• Stan example - requires installing R. Windows users will also need Rtools, Mac users may need the toolchain and corresponding tools
• INLA examples - requires installing R. To get the INLA package for R, follow the non-standard procedure here

• The R site, which includes the Comprehensive R Archive Network (CRAN) of downloads and packages
• A reference card of common R commands (and a slightly longer reference card)

• A First Course in Bayesian Statistical Methods (Hoff, 2009) - available free with UW login
• Bayesian and Frequentist Regression Methods (Wakefield, 2013) - available from with UW login
• Foulkes, Applied Statistical Genetics with R For Population-based Association Studies, 2009; this book covers a lot of the material we'll discuss, although seldom from a Bayesian point of view.