BIS 567A: Bayesian Statistics; Yale University, Fall 2015-Present

Course Description:  Bayesian inference is a method of statistical inference where prior beliefs for model parameters can be incorporated into an analysis and updated once data are observed. This course is designed to provide an introduction to basic aspects of Bayesian data analysis including conceptual and computational methods. Broad major topics include Bayes theorem, prior distributions, posterior distributions, predictive distributions, and Markov chain Monte Carlo sampling methods. We will begin by motivating the use of Bayesian methods, discussing prior distribution choices in common single parameter models, and summarizing posterior distributions in these settings. Next, we will introduce computational methods needed to study multi-parameter models. R software will most often be used. We will then apply these methods to more complex modeling settings including linear, generalized linear, and hierarchical models. Discussion of model comparisons and adequacy will also be presented.