Introduction to Applied Bayesian Methods

Catalog description

Credits 3. 3 Lecture Hours. Uncertainty regarding parameters and how they can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution; emphasis on modeling and computations under the Bayesian paradigm; includes prior distributions, Bayes Theorem, conjugate and non-conjugate models, posterior simulation via the Gibbs sampler and MCMC, hierarchical modeling. Prerequisites: STAT 630 , or equivalent or approval of instructor.