Computational Bayesian Inference

This course is an introduction to Bayesian inference and Markov Chain Monte Carlo computational methods. We begin with an overview of the principles of Bayesian inference and study a number of classic conjugate models. Students then learn the basic concepts of Markov chains and their convergence to an equilibrium distribution. Finally, we explore the celebrated Metropolis-Hastings algorithm and the Gibbs sampler, and see how these can be used to estimate a variety of Bayesian models. All coursework is completed using the R programming language in the RStudio environment. No prior knowledge of Bayesian inference is required, nor is familiarity with R assumed.