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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: henning@ruc.dk, cth@ruc.dk, otl@ruc.dk, petit@ruc.dk)
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: henning@ruc.dk, cth@ruc.dk, otl@ruc.dk, petit@ruc.dk)
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: henning@ruc.dk, cth@ruc.dk, otl@ruc.dk, petit@ruc.dk)
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: henning@ruc.dk, cth@ruc.dk, otl@ruc.dk, petit@ruc.dk)
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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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References

Chang, M.-W., Ratinov, L.-A., and Rizzolo, N.Roth, D. 2008. Learning and inference with constraints. In Proc. of AAAI Conference on Artificial Intelligence. Chicago, USA, 1513–1518.Google Scholar
Christiansen, H. and Gallagher, J. 2009. Non-discriminating arguments and their uses. In Proc. of Intermational Conference in Logic Programming. Pasadena, USA, 55–69.Google Scholar
Christiansen, H., Have, C., Lassen, O., and Petit, M. 2009. A constraint model for constrained hidden markov model: A first biological application. In Proc. of the International Workshop on Constraint Based Methods for Bioinformatics. Lisbon, Portugal, 19–26.Google Scholar
Costa, V., Page, D., and Cussens, J. 2008. CLP(BN): Constraint logic programming for probabilistic knowledge. Probabilistic Inductive Logic Programming LNAI 4911, 156–188.Google Scholar
Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. 1998. Biological Sequence Analysis. Cambridge University Press.CrossRefGoogle Scholar
Landwehr, N., Mielikinen, T., Eronen, L., Toivonen, H., and Mannila, H. 2007. Constrained hidden markov models for population-based haplotyping. BMC Bioinformatics 8, S-2.CrossRefGoogle ScholarPubMed
Larrosa, J. and Schiex, T. 2004. Solving weighted CSP by maintaining arc consistency. Artificial Intelligence 159, 1–2, 1–26.CrossRefGoogle Scholar
Rabiner, L. 1989. A tutorial on hidden markov models and selected applications in speech recognitation. IEEE 77, 2 (February), 257–286.CrossRefGoogle Scholar
Riezler, S. 1998. Probabilistic Constraint Logic Programming. PhD thesis, University of Tübingen.Google Scholar
Roth, D. and Yih, W. 2005. Integer linear programming inference for conditional random fields. In Proc. of the International Conference on Machine Learning. Bonn, Germany, 737–744.Google Scholar
Roweis, S. 1999. Constraint hidden markov models. In Proc. of the International Conference of Advances in Neural Information Processing System. Denver, USA, 782–788.Google Scholar
Sato, T. 1995. A statistical learning method for logic programs with distribution semantics. In Proc. of International Conference in Logic Programming. Tokyo, Japan, 715–729.Google Scholar
Sato, T. 2000. A viterbi-like algorithm and em learning for statistical abduction. In Proc. of the Workshop on Fusion of Domain Knowledge with Data for Decision Support. Tokyo, Japan.Google Scholar
Sato, T., Kameya, T., and Zhou, N. 2005. Generative modeling with failure in PRISM. In Proc. of International Joint Conference on Aritificial Intelligence. Edinburgh, Scotland, 847–852.Google Scholar
Sato, T. and Kameya, Y. 1997. PRISM: A language for symbolic-statistical modeling. In Proc. of the International Joint Conference of on Artificial Intellingence. Nagoya, Japan, 1330–1335.Google Scholar
Sato, T. and Kameya, Y. 2008. New advances in logic-based probabilistic by PRISM. In Probabilistic Inductive Logic Programming. LNCS. Springer, 118–155.CrossRefGoogle Scholar
Van Hentenryck, P., Saraswat, V., and Deville, Y. 1995. Design, implementation, and evaluation of the constraint language cc(fd). Constraint Programming 910, 293–316.Google Scholar
Viterbi, A. J. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13, 260–269.CrossRefGoogle Scholar