ST 776 - Bayesian Inference

(3.00 cr.)

Provides an introduction to Bayesian methods with an emphasis on modeling and applications. The following topics are covered: the likelihood function, Bayes' Theorem, and prior and posterior distributions. The following distributions are examined: Binomial, Poisson, exponential, and normal. The comparison of two normal distributions and Bayesian linear regression are studied, as are Bayesian estimation and testing, predictive distributions, assessment of model assumptions, robustness of inference, and hierarchical Bayesian models. Markov Chain Monte Carlo (MCMC) approaches to fitting Bayesian models are introduced.

Prerequisite: ST 765 .
Sessions Typically Offered: Varies
Years Typically Offered: Varies