Overview
Description
Statistical inference using Bayes’ Theorem. Topics include posterior analysis for continuous and discrete random variables, prior specification, Bayesian regression, multivariate inference, and posterior sampling through Markov Chain Monte Carlo.
Requirements
Prerequisites
STAT 352STAT 467STAT 667
Recommended Preparation
STAT 445STAT 645
Original catalog text
Prerequisites
STAT 352 or STAT 467 or STAT 667.
Recommended Preparation
STAT 445 or STAT 645.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- demonstrate understanding of the concepts that underlie Bayesian inference and compare the results to frequentist alternatives.
- conduct Bayesian inference analytically and interpret the results.
- perform a Bayesian analysis using professional statistical packages (e.g., Minitab, R, and Stan).
- synthesize course concepts to apply Bayesian modeling techniques to real-world data in the pursuit of scientific inquiry.