Statistics 697B - ST- Bayesian Statistics

This course will introduce students to Bayesian data analysis, including modeling and computation. We will begin with a description of the components of a Bayesian model and analysis (including the likelihood, prior, posterior, conjugacy, non-informativeness, credible intervals, etc.), and illustrate these objects in simple models. We will then develop Bayesian approaches to more complicated models. The course will introduce Markov chain Monte Carlo methods, and students will have the opportunity to learn to use the WinBUGS and R open source statistical packages for computation.

Open to Graduate students only. PreReq: STATISTC 607 & 608