Spring
4
Bayes' theorem, prior and posterior distributions, likelihood functions, Bayesian data analysis, comparison of Bayesian and classical (frequentist) approaches, introduction to Markov Chain Monte Carlo methods (MCMC), hierarchical modeling and regression Bayesian analysis. A professional statistical software package will be used throughout the course.
Prerequisites: MATH 211 or MATH 212 or MATH 305 or ECON 290.