This course covers the theoretical and applied foundations of Bayesian
statistical analysis with an emphasis on computational tools for
Bayesian hierarchical models. We will discuss model checking, model
assessment, and model comparison. The course will cover Bayesian
stochastic simulation (Markov chain Monte Carlo) in depth with an
orientation towards deriving important properties of the Gibbs sampler
and the Metropolis Hastings algorithm. Extensions and hybrids will be