MAT 329 - Bayesian Analysis and Decision Making

An introduction to Bayesian analysis and inference. It covers many of the topics covered in a standard frequentist survey course from a Bayesian perspective. Topics include gathering datasets, summarizing datasets, probability, Bayes Theorem, Bayesian inference for discrete random variables, Bayesian inference for continuous random variables, comparing Bayesian and frequentist approaches to inference, Bayesian prediction intervals for single mean and single proportion, Bayesian inference for two means and two proportions, and Bayesian inference for linear regression.

Prerequisite(s): MAT 221.

3 credit(s).

Last Term Offered: Fall 2020