Bayesian Data Analysis

Catalog description

Introduction to Bayesian statistical methods and their application in fields such as agriculture, biology, engineering and medicine; topics include non-informative, conjugate and elicited priors; posterior development; common single and multiple parameter models such as binomial, normal, Poisson, and exponential; hierarchical models; hypothesis testing and credible sets; posterior simulation via Markov Chain Monte Carlo; and performance of Bayesian procedures.