MATH 538

Bayesian Statistics

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

Units: 3.0