MATH 538 - Bayesian Statistics (3)

Fundamentals of Bayesian inference including informative and noninformative priors for single and multiparameter models, Bayesian asymptotics, hierarchical models, Metropolis Hastings and Gibbs sampler algorithms, model checking, Bayesian design of experiments, Bayesian linear models and generalized linear models, and neural networks.

Prerequisites: MATH 530, MATH 534.

Graduate-level

One or more sections may be offered in any online format.