Inference with Constrained Hidden Markov Models in PRISM

TL;DR

This paper introduces a framework for extending Hidden Markov Models with side-constraints using Constraint Logic Programming, enabling more efficient inference and application to biological sequence alignment.

Contribution

It presents a novel PRISM-based approach for incorporating side-constraints into HMMs, improving expressiveness and inference efficiency.

Findings

Effective integration of constraints like cardinality and all-different

Enhanced inference efficiency demonstrated on biological sequence alignment

Framework facilitates more compact and pruning-friendly HMM representations

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.