MATH 346 - Bayesian Statistics

An introduction to the philosophy and practice of Bayesian statistics, an alternative framework to the classical frequentist approach. The course starts with foundational topics including Bayes’ theorem, conjugacy, and the philosophical and practical differences between Bayesian and frequentist approaches. We then take a deep dive into regression, hierarchical models, computational methods, and other advanced topics among missing data, mixture models, and prediction, all from a Bayesian perspective. Emphasis is placed on applying Bayesian methods to real-world datasets.

Unit(s): 1
Group Distribution Requirement(s): Distribution Group III
Prerequisite(s): MATH 141 and MATH 243
Instructional Method: Lecture-conference
Grading Mode: Letter grading (A-F)
Not offered: 2024-25
Group Distribution Learning Outcome(s):

Use and evaluate quantitative data or modeling, or use logical/mathematical reasoning to evaluate, test or prove statements.
Given a problem or question, formulate a hypothesis or conjecture, and design an experiment, collect data, or use mathematical reasoning to test or validate it.