Large Deviations Approach to Bayesian Nonparametric Consistency: the Case of Polya Urn Sampling

The Bayesian Sanov Theorem (BST) identifies, under both correct and incorrect specification of infinite dimensional model, the points of concentration of the posterior measure. Utilizing this insight in the context of Polya urn sampling, Bayesian nonparametric consistency is established. Polya BST is also used to provide an extension of Maximum Non-parametric Likelihood and Empirical Likelihood methods to the Polya case.


Issue Date:
2007
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/6056
Total Pages:
6
Series Statement:
CUDARE Working Paper
1048




 Record created 2017-04-01, last modified 2017-08-23

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