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Statistical Inference in Hidden Markov Models Using k-Segment Constraints

Abstract:

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward–backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-...

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

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Publisher copy:
10.1080/01621459.2014.998762

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Department:
Oxford, MSD, NDM, Human Genetics Wt Centre
Role:
Author
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Department:
Oxford, MSD, NDM, Human Genetics Wt Centre
Role:
Author
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Institution:
University of Oxford
Department:
Oxford, MSD, NDM, Human Genetics Wt Centre
Role:
Author
Wellcome Trust More from this funder
Medical Research Council More from this funder
Publisher:
Taylor and Francis Publisher's website
Journal:
Journal of the American Statistical Association Journal website
Volume:
111
Issue:
513
Pages:
200-215
Publication date:
2016-05-05
Acceptance date:
2015-01-29
DOI:
EISSN:
1537-274X
ISSN:
0162-1459
URN:
uuid:1dd90c62-4cb5-45b8-b338-8827fc40114a
Source identifiers:
627084
Local pid:
pubs:627084

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