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Daniel B. Rowe, Ph.D.
MATH 4790/MSSC 5790: Bayesian Statistics
There is not a required book. All material will be presented via lecture

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Course Syllabus Fall 2025
Flyer, Syllabus
Generally one class period will be used to cover a particular topics.
We will also go through your homework and Matlab code in class.

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Course Lectures
Timeline
Lecture 00: Introductions & Syllabus
Lecture 01: Introduction to Matlab
Lecture 01: Events, Probabilities, and Bayes’ Rule
Lecture 02: Discrete Probability Mass Functions (Bernoulli, Binomial, Multiomial)
Lecture 03: Continuous Probability Density Functions (Beta, Normal, Gamma, Inverse Gamma)
Lecture 04: Bivariate Probability Density Functions (Normal)
Lecture 05: Bivariate Probability Density Functions Continued (Student-t)
Lecture 06: Symmetric Matrix PDFs (Wishart and Inverse Wishart)
Lecture 07: Maximum Likelihood Estimation (Univariate, Multivariate, and Regression)
Lecture 08: Bayes' Rule and Conjugate Priors (Binomial and Normal)
Lecture 09: Bayesian Non-Conjugate Priors (Deterministic and Stochastic Integration for Binomial)
Lecture 10: Bayesian Multivariate Normal (Exact Theoretical and Gibbs Sampling Marginals)
Lecture 11: Bayesian Multiple Regression (Ridge, LASSO, and Elastic Net)
Lecture 12: Bayesian Multivariate Regression (Matrix Normal and Student-T)
Lecture 13: Bayesian Classification (Bayes and Naive Bayes)
Lecture 14: Bayesian Complex-Valued Latent Regression (Chase)


All topics and assignments will have a computational aspect using Matlab.
Deterministic numerical and stochastic simulation integration sprinkled in above.

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Return to Professor Rowe's Webpage

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