JSM2019

R code and R Markdown files for JSM short course on Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling

Outline:

1. Why Bayes? (some advantages of a Bayesian perspective)

2. Normal Inference (introduction to the Bayesian paradigm and computation)

3. Overview of Bayesian Computation (discussion of computational strategies and software)

4. Regression (introduction to Bayesian regression)

5. Federalist Paper Study (models for count data)

6. Attendance Data (beta regression model for fraction response data)

7. BBS Survey (introduction to multilevel modeling)

8. Coffee Shop Waiting Times (multilevel regression model)

9. Latent Data (introduction to latent modeling

Books:

Albert, J. (2009) Bayesian Computation using R, 2nd edition, Springer.

McElreath, R. (2015) Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Chapman and Hall.

Gelman, A. and Hill, J. (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge.

Albert, J. and Hu, Jingchen (2019) Probability and Bayesian Modeling, Chapman and Hall (in progress)

R Packages:

• LearnBayes, version 2.15, available on CRAN.

• rethinking, version 1.59, available on github

• brms, version 2.9.0, available on CRAN and github

• rstan, version 2.18.2, available on CRAN

• rstanarm, version 2.18.2, available on CRAN

• runjags, version 2.04-2, available on CRAN (also need JAGS software available on https://sourceforge.net/projects/mcmc-jags)