STAT 646 - Introduction to Bayesian Statistics

(3 units)

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.

Prerequisite(s): STAT 352 or STAT 467 or STAT 667. Recommended preparation: STAT 445 or STAT 645.

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. demonstrate understanding of the concepts that underlie Bayesian inference and compare the results to frequentist alternatives.
2. conduct Bayesian inference analytically and interpret the results.
3. perform a Bayesian analysis using professional statistical packages (e.g., Minitab, R, and Stan).
4. synthesize course concepts to apply Bayesian modeling techniques to real-world data in the pursuit of scientific inquiry.

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