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
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.