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# 1 - 3 of 3 results for: STATS 270: A Course in Bayesian Statistics

## STATS 270:A Course in Bayesian Statistics (STATS 370)

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference.
Terms: Win | Units: 3

## STATS 271:Bayesian Statistics II (STATS 371)

This is the second of a two course sequence on modern Bayesian statistics. Topics covered include: Asymptotic properties of Bayesian procedures and consistency (Doobs theorem, frequentists consistency, counter examples); connections between Bayesian methods and classical methods (the complete class theorem); generalization of exchangeability; general versions of the Bayes theorem in the undominated case; non parametric Bayesian methods (Dirichelet and Polya tree priors). Throughout general theory will be illustrated with classical examples. Prerequisites: Stats 270/370.
Last offered: Spring 2016

## STATS 371:Bayesian Statistics II (STATS 271)

This is the second of a two course sequence on modern Bayesian statistics. Topics covered include: Asymptotic properties of Bayesian procedures and consistency (Doobs theorem, frequentists consistency, counter examples); connections between Bayesian methods and classical methods (the complete class theorem); generalization of exchangeability; general versions of the Bayes theorem in the undominated case; non parametric Bayesian methods (Dirichelet and Polya tree priors). Throughout general theory will be illustrated with classical examples. Prerequisites: Stats 270/370.
Last offered: Spring 2016
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