Lawrence Joseph
Bayesian statistics

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Course outline
Basic information
Instructor Lawrence Joseph
Credits 2
Course Objectives and Topics CoveredTo provide researchers with an introduction to practical Bayesian methods. Topics will include Bayesian philosophy, simple univariate models, linear and logistic regression and hierarchical models. Numerical techniques including Monte Carlo integration, sampling importance resampling (SIR), and the Gibbs sampler will be covered, including programming in R and WinBUGS.
TimeThursdays 12:30 PM - 2:30 PM (September 7 - November 30).
PlacePurvis Hall Room 25
AssessmentFive assignments, worth 20% each. There will be no midterm or final exams.
Textbook (Reference only)Andrew Gelman, John Carlin, Hal Stern and Donald Rubin. Bayesian Data Analysis, 2nd Edition, Chapman and Hall, 2003. Textbook review of Gelman et al
PrerequisitesEPIB 607 and EPIB 621 or permission from the instructor.