STA 654 - APPLIED BAYESIAN INFERENCE

College of Arts and Sciences

Credits: 3

This course provides an introduction to Bayesian inference and a summary of Bayesian methods for fitting, assessing, and selecting models. Topics include Bayes’ Rule and Probability, Binomial Models for Proportions, Poisson Models for Counts, Normal Models for Continuous Data, Linear Regression, Log-linear and Contingency Tables, Hierarchical Models, Hypothesis Testing, Model Comparison, and Selected Applications.

Prerequisite(s):
Prereq: Graduate status in Master of Applied Statistics, STA 646, STA 648.
Approved for Distance Learning.