Distributions associated with general runs and patterns in hidden Markov models

TL;DR

This paper introduces a method to compute distributions of patterns in hidden Markov models' state sequences, conditioned on observed data, applicable to diverse problems including biological sequences.

Contribution

It develops a general framework for calculating pattern distributions in hidden Markov models with complex patterns and dependencies, including biological applications.

Findings

Method effectively computes pattern probabilities in HMMs.

Applicable to DNA sequence analysis with biological restrictions.

Demonstrated through illustrative and real biological examples.

Abstract

This paper gives a method for computing distributions associated with patterns in the state sequence of a hidden Markov model, conditional on observing all or part of the observation sequence. Probabilities are computed for very general classes of patterns (competing patterns and generalized later patterns), and thus, the theory includes as special cases results for a large class of problems that have wide application. The unobserved state sequence is assumed to be Markovian with a general order of dependence. An auxiliary Markov chain is associated with the state sequence and is used to simplify the computations. Two examples are given to illustrate the use of the methodology. Whereas the first application is more to illustrate the basic steps in applying the theory, the second is a more detailed application to DNA sequences, and shows that the methods can be adapted to include restrictions related to biological knowledge.