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Course: STAT 35920=PBHS 43010
Title: Applied Bayesian Modeling and Inference
Instructor(s): Yuan Ji
Class Schedule: Sec 01: W 10:30AM–1:20PM in BSLC 313
Office Hours:  
Textbook(s):  
Description: Course begins with basic probability and distribution theory, and covers a wide range of topics related to Bayesian modeling, computation, and inference. Significant amount of effort will be directed to teaching students on how to build and apply hierarchical models and perform posterior inference. The first half of the course will be focused on basic theory, modeling, and computation using Markov chain Monte Carlo methods, and the second half of the course will be about advanced models and applications. Computation and application will be emphasized so that students will be able to solve real-world problems with Bayesian techniques.

Prerequisite(s): STAT 24400 and STAT 24500 or master level training in statistics.

Note(s): At least one course in linear regression and basic familiarity with STAT; or consent of instructor.