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Inference with constrained hidden Markov models in PRISM

Published online by Cambridge University Press:  09 July 2010

HENNING CHRISTIANSEN ,
CHRISTIAN THEIL HAVE ,
OLE TORP LASSEN  and
MATTHIEU PETIT
HENNING CHRISTIANSEN
Affiliation:
Research Group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O.Box 260, DK-4000 Roskilde, Denmark (e-mail: [email protected], [email protected], [email protected], [email protected])
CHRISTIAN THEIL HAVE
Affiliation:
Research Group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O.Box 260, DK-4000 Roskilde, Denmark (e-mail: [email protected], [email protected], [email protected], [email protected])
OLE TORP LASSEN
Affiliation:
Research Group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O.Box 260, DK-4000 Roskilde, Denmark (e-mail: [email protected], [email protected], [email protected], [email protected])
MATTHIEU PETIT
Affiliation:
Research Group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O.Box 260, DK-4000 Roskilde, Denmark (e-mail: [email protected], [email protected], [email protected], [email protected])
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Abstract

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.

Keywords

hidden Markov model with side-constraintsinferenceprogramming in statistical modeling
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 4-6: 26th International Conference on Logic Programming , July 2010 , pp. 449 - 464
Copyright
Copyright © Cambridge University Press 2010

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