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Sufficientness postulates for Gibbs-type priors and hierarchical generalizations

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 "suff...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's copy

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Publisher copy:
10.1214/17-STS619

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
Publisher:
Institute of Mathematical Statistics (IMS) Publisher's website
Journal:
Statistical Science Journal website
Volume:
32
Issue:
4
Pages:
487-500
Publication date:
2017-11-28
Acceptance date:
2017-06-10
DOI:
EISSN:
2168-8745
ISSN:
0883-4237
Pubs id:
pubs:701409
URN:
uri:101ed398-9c63-4ffe-8c3c-ce2eb52bb2c1
UUID:
uuid:101ed398-9c63-4ffe-8c3c-ce2eb52bb2c1
Local pid:
pubs:701409

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