Statistics 220 - Bayesian Modeling

Bayesian statistics is founded upon the idea that our beliefs about the world are constantly revised with the incorporation of new information. This course provides a principled introduction to Bayesian statistics. We begin with the basic building blocks of Bayesian inference: the likelihood, prior, and posterior distributions. We will then show how to simulate from the posterior distribution using the Markov chain Monte Carlo (MCMC) method. Single and multivariate models will be considered as well as hierarchical models, such as Bayesian linear regression, and other more advanced topics. The course will emphasize problem solving and data analysis via statistical software.  Four class hours per week. 

Requisite MATH 111 and STAT 111/135 or permission of the instructor.  Limited to 20 students. Spring semester.  Professor Wang.