Journal article
Statistical Inference in Hidden Markov Models Using k-Segment Constraints
- Abstract:
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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
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(Version of record, pdf, 1.2MB)
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- Publisher copy:
- 10.1080/01621459.2014.998762
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Authors
Funding
Medical Research Council
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Wellcome Trust
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Bibliographic Details
- 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:
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1537-274X
- ISSN:
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0162-1459
- Source identifiers:
-
627084
Item Description
- Keywords:
- Pubs id:
-
pubs:627084
- UUID:
-
uuid:1dd90c62-4cb5-45b8-b338-8827fc40114a
- Local pid:
- pubs:627084
- Deposit date:
- 2016-06-14
Terms of use
- Copyright holder:
- Titsias et al
- Copyright date:
- 2016
- Notes:
- © 2016 The Author(s). Published with license by Taylor and Francis © Michalis K. Titsias, Christopher C. Holmes, and Christopher Yau. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.
- Licence:
- CC Attribution (CC BY)
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