MATH 355 - Bayesian Statistics

Bayesian statistics, an alternative to the traditional frequentist approach taken in our other statistics courses, is playing an increasingly integral role in modern statistics. Highlighted by Nate Silver of fivethirtyeight.com and Baseball Prospectus fame, Bayesian statistics has even reached a popular audience. This course explores the Bayesian philosophy, the Bayesian approach to statistical analysis, Bayesian computing, as well as both sides of the frequentist versus Bayesian debate. Topics include Bayes’ Theorem, prior and posterior probability distributions, Bayesian regression, Bayesian hierarchical models, and an introduction to Markov chain Monte Carlo techniques. This course counts toward the capstone requirement. Prerequisite(s): MATH 155, and MATH 253 or MATH 254 (highly recommended) Every spring semester (4 Credits)