Applied Bayesian Modeling

Catalog description

Introduction to Bayesian inference and modeling. Topics include conjugate priors, posterior sampling, Markov Chain Monte Carlo (MCMC) methods, hierarchical models, and model diagnostics. Emphasis on applications and computational tools, including modern probabilistic programming languages. Students will learn to analyze data using Bayesian methods, interpret posterior distributions, and communicate findings effectively. Prerequisite: a minimum grade of 2.0 in either TSTAT 345 or TMATH 390.