Courses

You can view the current schedule of SED courses to see the exact dates for any Bayesian related courses. You can also check registration information.

This course will introduce the basic concepts of the Bayesian approach to statistics such as the subjective interpretation of probability, the types of prior distributions, the use of Bayes theorem in updating information and inference procedures such as Bayes estimators and HPD regions. At the conclusion of the course, the students will be able to assess their prior knowledge and transform it into a prior distribution for a univariate problem, then combine their prior information with data to arrive at their posterior distribution. They will then be able to evaluate the posterior and calculate posterior probabilities of their hypotheses of interest or calculate HPD regions for the parameters.

You can view the course notes for this class.

This course will present, empirical Bayes and hierarchical Bayes analysis of the Linear Model. There will be an emphasis on computation using MCMC algorithms with particular emphasis on the use of the BUGS software. At the conclusion of the course, the students will be able to perform Bayesian analysis of regression as well as ANOVA models. They will be able to evaluate the convergence of the MCMC simulations and will be able to calculate HPD regions and perform hypothesis tests.

This was a 12 hour course that presented both the classical and the Bayesian approaches to uncertainty.

Course: Uncertainty: Classical and Bayesian Methods
Instructors: Mark Levenson, Mark Vangel, and David Banks
Date: 1998

This was a 12 hour course that presented both the classical and the Bayesian approaches to uncertainty.

Date created: 8/28/2001
Last updated: 9/6/2001
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