A constructive proof of the existence of Viterbi processes

TL;DR

This paper provides a constructive proof demonstrating the existence of infinite Viterbi alignments for a broad class of hidden Markov models, advancing theoretical understanding of their long-term behavior.

Contribution

It offers a constructive proof of the existence of infinite Viterbi alignments applicable to a wide range of HMMs, improving upon previous existential proofs with stronger generality.

Findings

Proves the existence of infinite Viterbi alignments constructively.

Applicable to a very general class of HMMs.

Enhances theoretical understanding of Viterbi processes.

Abstract

Since the early days of digital communication, hidden Markov models (HMMs) have now been also routinely used in speech recognition, processing of natural languages, images, and in bioinformatics. In an HMM $(X_i,Y_i)_{i\ge 1}$, observations $X_1,X_2,...$ are assumed to be conditionally independent given an ``explanatory'' Markov process $Y_1,Y_2,...$, which itself is not observed; moreover, the conditional distribution of $X_i$ depends solely on $Y_i$. Central to the theory and applications of HMM is the Viterbi algorithm to find {\em a maximum a posteriori} (MAP) estimate $q_{1:n}=(q_1,q_2,...,q_n)$ of $Y_{1:n}$ given observed data $x_{1:n}$. Maximum {\em a posteriori} paths are also known as Viterbi paths or alignments. Recently, attempts have been made to study the behavior of Viterbi alignments when $n\to \infty$. Thus, it has been shown that in some special cases a well-defined limiting Viterbi alignment exists. While innovative, these attempts have relied on rather strong assumptions and involved proofs which are existential. This work proves the existence of infinite Viterbi alignments in a more constructive manner and for a very general class of HMMs.