Biostats 202C

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Course Description

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:

  1. the difference between Bayesian and classical inference;
  2. the theory and mechanics of Bayesian inference;
  3. how to fit Bayesian models algebraically and how models combine data with prior belief to make inference;
  4. computing for Bayesian inference;
  5. Bayesian approaches to regression.

Time and Location

Mondays 1-250pm @ CHS 61-235
Wednesdays 1-150pm @ CHS 61-235

Instructor

Andrew J. Holbrook
Assistant Professor of Biostatistics
UCLA Fielding School of Public Health
Email: aholbroo@g.ucla.edu
Office hours: Wednesdays 2-250pm @ CHS 76-062A

Syllabus

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