535—Introduction to Bayesian Data Analysis. (3) (Prereq: A grade of C or higher in STAT 512; or CSCE 582 [=STAT 582]; or both STAT 511 and either STAT 509 or STAT 515; or equivalent) Principles of Bayesian statistics, including: one- and multi-sample analyses; Bayesian linear models; Monte Carlo approaches; prior elicitation; hypothesis testing and model selection; hierarchical models; selected advanced models; statistical packages such as WinBUGS and R.
Sample Course Homepage: Recent Semester
Usually Offered: Spring Semesters, Even Years
Learning Objectives: By the end of the term successful students should be able to do the following:
- Understand the philosophy of Bayesian statistical modeling
- Understand Bayesian models for numerous common data analysis situations, including prior elicitation
- Use software such as R and BUGS to implement Bayesian analyses
- Understand basic principles of both conjugate analyses and MCMC-based Bayesian analyses
Current Textbook: Bayesian Methods (Second Edition), by Jeff Gill, Chapman & Hall/CRC Press, 2008.
|Review of Probability Concepts||1 week|
|Bayes' Law and the Basic Bayesian Framework||1 week|
|Bayesian Analyses for Basic One-Sample Models||1.5 weeks|
|Bayesian Linear Models||1.5 weeks|
|General Classes of Prior Distributions and Prior Elicitation||1 week|
|Some Useful Monte Carlo Methods (along with use of R and BUGS)||1.5 weeks|
|Assessing Model Quality||1.5 weeks|
|Bayesian Hypothesis Testing||1 week|
|Bayesian Analyses for Two- and k-Sample Models||1 week|
|Hierarchical Bayesian Models||1 week|
|Advanced Bayesian Models: Count Regression, Mixed Models,
Models for Clustered/Longitudinal Data (Time permitting)
The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.
Contact Faculty: David Hitchcock