Home

This course is designed to provide an introduction to fundamental conceptual, computational, and practical methods of Bayesian data analysis. The first part will introduce the Bayesian approach, including

• Comparison with frequentist methods
• Bayesian learning
• Common prior distributions
• Summarizing posterior distributions

In order to study problems with more than a few parameters, modern Bayesian computing algorithms are required. In the second part of the course we will study these methods, mostly MCMC and mostly using R. The main topics include

• Monte Carlo approximation
• Gibbs sampling
• Convergence diagnostics
• JAGS

With these computational tools at hand, we will begin applying Bayesian methods using

• Multiple linear regression
• Generalized linear models
• Hierarchical models

We will pay special attention to comparing models with each other and testing for model adequacy.

For more information, view the course syllabus.