Inference with constrained hidden Markov models in PRISM

Henning Christiansen, Christian Theil Have, Ole Torp Lassen, Matthieu Petit

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Resumé

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming has advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.
OriginalsprogEngelsk
TidsskriftTheory and Practice of Logic Programming
Vol/bind10
Udgave nummer4-6
Sider (fra-til)449-464
Antal sider15
ISSN1471-0684
DOI
StatusUdgivet - 2010

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Inference with constrained hidden Markov models in PRISM. / Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp; Petit, Matthieu.

I: Theory and Practice of Logic Programming, Bind 10, Nr. 4-6, 2010, s. 449-464.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Inference with constrained hidden Markov models in PRISM

AU - Christiansen, Henning

AU - Have, Christian Theil

AU - Lassen, Ole Torp

AU - Petit, Matthieu

PY - 2010

Y1 - 2010

N2 - A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming has advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.

AB - A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming has advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.

KW - inferens

KW - Hidden Markov Model with side-constraints

KW - inference

KW - Programming in statistical modeling

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DO - 10.1017/S1471068410000219

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