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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
• Machine learning

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

The course is based on Reich and Ghosh (2026), Bayesian Statistical Methods with Applications to Machine Learning.

For more information, view the course syllabus.