Bayesian Methodology in Biostatistics
BIOSTAT 249
Subject & Catalog Number
Course Information
Description
General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis.
Course Notes
Offered jointly with the School of Public Health as BST249.
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Biostatistics
Course Component
Lecture
Subject
Biostatistics
Grading Basis
FAS Letter Graded
Course Requirements
Prerequisite: Biostatistics 231 AND Biostatistics 232
General Education
N/A
Quantitative Reasoning with Data
N/A
Divisional Distribution
Science & Engineering & Applied Science
Course Level
Primarily for Graduate Students
