Introduction to Bayesian inference; prior and posterior distributions; conjugate priors; statistical inference, including summarizing posterior information and making predictions; hierachical and linear regression models. Monte Carlo methods including Gibbs sampling.
Prerequisites & Notes: MATH 342 and MATH 442 or instructor permission. Credits: 4 Grade Mode: Letter