摘要：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.
关键词：Polya L-divergence;Bayesian maximum (A posterior);Probability method;Maximum Non-Parametric Likelihood method;Empirical likelihood method
