Statistics 610 - 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 and credible intervals). We will then develop Bayesian approaches to models such as regression models, hierarchical models and ANOVA. Computing topics include Markov chain Monte Carlo methods. The course will have students carry out analyses using statistical programming languages and software packages.

Open to Graduate students only. PreReq: STATISTC 607 & 608 This class meets on the Newton Campus of UMass-Amherst. This course may be taken remotely. Please enroll and contact the instructor if you would like to take the course remotely.