Applied Bayesian Inference (457-0-1)
Instructors
Martin A Tanner
847/491-2700
2006 Sheridan Road
Meeting Info
STAT Sem Rm B02 - 2006 Sher: Tues, Thurs 9:30AM - 10:50AM
Overview of class
The purpose of this course is to provide an introduction to a variety of computational algorithms for Bayesian inference. Two types of methods are considered in detail: observed data and data augmentation methods. The observed data methods are applied directly to the likelihood or to the posterior density. These include: Newton- Raphson, Laplace's method, Monte Carlo and Metropolis methods. The data augmentation methods rely on an augmentation of the data which simplifies the likelihood or posterior density. These include: EM, Data Augmentation, Poor Man's Data Augmentation and the Gibbs sampler. All methods are motivated and illustrated with real examples.
Registration Requirements
Stat 420-1,2,3 are prerequisites
Learning Objectives
This course provides the student with a good introduction to Bayesian inference, the ability to read application papers, and the ability to apply these methods to problems of interest to the student. Students understand at a heuristic level how the methods work and when a given method may be preferred over another.
Teaching Method
Lecture
Evaluation Method
Weekly homeworks
Class Materials (Required)
Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions by M. Tanner THIRD EDITION ONLY required http://www.springer.com/us/book/9780387946887
Bayesian Methods by Jeff Gill THIRD EDITION ONLY recommended https://www.crcpress.com/Bayesian-Methods-A-Social-and-Behavioral-Sciences-Approach-Third-Edition/Gill/p/book/9781439862483
Class Materials (Suggested)
Bayesian Statistical Methods by Brian J. Reich, Sujit K. Ghosh
https://www.routledge.com/Bayesian-Statistical-Methods/Reich-Ghosh/p/book/9781032093185
Enrollment Requirements
Enrollment Requirements: Prerequisites: STAT 350-0 and STAT 420-1 and STAT 420-2 and STAT 420-3 or equivalent or students who have earned a Master’s degree in Statistics or permission of the instructor
Add Consent: Instructor Consent Required