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.

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Year of publication:	2007-09-21
Authors:	Grendar, Marian ; Judge, George G. ; Niven, R. K.
Institutions:	Department of Agricultural and Resource Economics, University of California-Berkeley
Subject:	Polya L-divergence \| Bayesian Maximum (A Posteriori) Probability method \| Maximum Non-parametric Likelihood method \| Empirical Likelihood method

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Extent:	application/pdf
Series:	Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Department of Agricultural and Resource Economics, UCB
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010537510