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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
Version:
Publisher's version

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Titsias, M More by this author
Publisher:
Curran Associates, Inc. Publisher's website
Volume:
27
Pages:
2960-2968
Publication date:
2014
ISSN:
1049-5258
URN:
uuid:8ad7137d-7f0f-42f3-b0e2-2f07349a0deb
Source identifiers:
515253
Local pid:
pubs:515253

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