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Hamming ball auxiliary sampling for factorial hidden Markov models

Abstract:

We introduce a novel sampling algorithm for Markov chain Monte Carlo-based Bayesian inference for factorial hidden Markov models. This algorithm is based on an auxiliary variable construction that restricts the model space allowing iterative exploration in polynomial time. The sampling approach overcomes limitations with common conditional Gibbs samplers that use asymmetric updates and become easily trapped in local modes. Instead, our method uses symmetric moves that allows joint updating of...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Funding agency for:
Yau, C
Grant:
MR/L001411/1
Publisher:
Curran Associates, Inc. Publisher's website
Volume:
27
Pages:
2960-2968
Host title:
Advances in Neural Information Processing Systems
Publication date:
2014-01-01
Event location:
Montreal, Canada
ISSN:
1049-5258
Source identifiers:
515253
Pubs id:
pubs:515253
UUID:
uuid:8ad7137d-7f0f-42f3-b0e2-2f07349a0deb
Local pid:
pubs:515253
Deposit date:
2015-03-30

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