Inference with Constrained Hidden Markov Models in PRISM
Artificial Intelligence
2010-08-02 v1 Logic in Computer Science
Programming Languages
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 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.
Cite
@article{arxiv.1007.5421,
title = {Inference with Constrained Hidden Markov Models in PRISM},
author = {Henning Christiansen and Christian Theil Have and Ole Torp Lassen and Matthieu Petit},
journal= {arXiv preprint arXiv:1007.5421},
year = {2010}
}