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Applied Bayesian Analysis
BST 228

Course Information

Description

This course is a practical introduction to the Bayesian analysis of biomedical data. It is an intermediate Master's level course in the philosophy, analytic strategies, implementation, and interpretation of Bayesian data analysis. Specific topics that will be covered include: the Bayesian paradigm; Bayesian analysis of basic models; Bayesian computing: Markov Chain Monte Carlo; STAN R software package for Bayesian data analysis; linear regression; hierarchical regression models; generalized linear models; meta-analysis; models for missing data.

Programming and case studies will be used throughout the course to provide hands-on training in these concepts.
Prerequisites: (BST210, BST 220 or PHS 2000A&B) and BST222, or permission of the instructor

School Harvard Chan School
Credits 5
Cross Reg

Available for Harvard Cross Registration

Department Biostatistics
Course Component Lecture
Instruction Mode In Person
Grading Basis HSPH Student Option (Audit, Ordinal, Pass/Fail)
Course Requirements Prerequisites: (BST210, BST 220 or PHS2000A&B) and BST222