Inference with Constrained Hidden Markov Models in PRISM

Details Inference with Constrained Hidden Markov Models in PRISM Inference with Constrained Hidden Markov Models in PRISM

2010-07-30

arxiv.org/abs/1007.5421v1

TPLP 2010, 10 (4-6) 449-464

Computer Science

Artificial Intelligence

Scientific paper

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 have 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.

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