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

M. Grendar, G. Judge and R. Niven
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M. Grendar: Institute of Measurement Sciences SAS, Bratislava, Slovakia
G. Judge: University of California, Berkeley and Giannini Foundation
R. Niven: The University of New South Wales at ADFA

No 1048, Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: 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.

Keywords: Polya L-divergence; Bayesian Maximum (A Posteriori) Probability method; Maximum Non-parametric Likelihood method; Empirical Likelihood method (search for similar items in EconPapers)
Date: 2007-09-21
Note: oai:cdlib1:are_ucb-1179
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