Sufficientness postulates for Gibbs-type priors and hierarchial generalizations

Bacallado, S. and Battiston, Marco and Favaro, Stefano and Trippa, L. (2017) Sufficientness postulates for Gibbs-type priors and hierarchial generalizations. Statistical Science, 32 (4). pp. 487-500. ISSN 0883-4237

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Abstract

A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assuming that the corresponding predictive probabilities obey certain properties. An early discussion of such a problem, although in a parametric framework, dates back to the seminal work by English philosopher W. E. Johnson, who introduced a noteworthy characterization for the predictive probabilities of the symmetric Dirichlet prior distribution. This is typically referred to as Johnson’s “sufficientness” postulate. In this paper, we review some nonparametric generalizations of Johnson’s postulate for a class of nonparametric priors known as species sampling models. In particular, we revisit and discuss the “sufficientness” postulate for the two parameter Poisson–Dirichlet prior within the more general framework of Gibbs-type priors and their hierarchical generalizations.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600
Subjects:
ID Code:
132139
Deposited By:
Deposited On:
21 Mar 2019 16:20
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Jul 2020 05:17