Course Description
Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS).
Fall 2024
Instructors
Meeting Patterns
Classes Start:
August 19, 2024
Classes End:
December 3, 2024
Location:
02106 SAS Hall
Class Days:
M W
Class Start Time:
1:30pm
Class End Time:
2:45pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
Prerequisite: ST 702 R: 17STPHD Students Only
