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Scalable Bayesian inference for the inverse temperature of a hidden Potts model

Abstract:

The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises from the dependence of an intractable normalising constant on the value of this parameter and thus there is no closed-form solution for sampling from the posterior distribution directly. There is a variety of computational approaches for sampling from the posterior without evaluating the normalising constant, includ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1214/18-BA1130

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
St Peter's College
Role:
Author
ORCID:
0000-0002-1595-9041
Publisher:
International Society for Bayesian Analysis
Journal:
Bayesian Analysis More from this journal
Publication date:
2018-12-12
Acceptance date:
2018-10-18
DOI:
EISSN:
1931-6690
ISSN:
1936-0975
Keywords:
Pubs id:
pubs:943533
UUID:
uuid:d3886433-2abc-4178-980d-aa3ba2b4aec0
Local pid:
pubs:943533
Source identifiers:
943533
Deposit date:
2018-11-16

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