Supplemental Materials

Welcome to the online supplemental materials for Bayesian Statistical Methods: With a Balance of Theory and Computation by Brian J. Reich and Sujit K. Ghosh.  Below we provide the data sets using in the book as R workspaces and step-by-step R/JAGS code for several worked examples.


Worked examples

Chapter 1 – Basics of Bayesian inference

Chapter 2 – From Prior Information to Posterior Inference

Chapter 3 – Computational Methods

Chapter 4 – Linear Models

Chapter 5 – Model Selection and Diagnostics

Chapter 6 – Case Studies Using Hierarchical Modeling

Chapter 7 – Statistical Properties of Bayesian Methods

Solutions to odd-numbered problems

Lecture notes

Code from the listings

Video lectures