This 4-unit course involves the mathematical underpinnings of the Bayesian approach to statistical inference; closed form computations; computation; hierarchical models; model selection; hypothesis testing; prior specification; comparative inference; nonparametric methods.
Students will understand:
- the difference between Bayesian and classical inference;
- the theory and mechanics of Bayesian inference;
- how to fit Bayesian models algebraically and how models combine data with prior belief to make inference;
- computing for Bayesian inference;
- Bayesian approaches to regression.
Time and Location
Mondays 1-250pm @ CHS 61-235
Wednesdays 1-150pm @ CHS 61-235
Andrew J. Holbrook
Assistant Professor of Biostatistics
UCLA Fielding School of Public Health
Office hours: Wednesdays 2-250pm @ CHS 76-062A