Equations for hidden Markov models

TL;DR

This paper introduces new theoretical methods to derive invariants for hidden Markov models by viewing random processes within a vector space framework, offering fresh insights into model analysis.

Contribution

It presents a novel theoretical framework for deriving model invariants for hidden Markov models using vector space techniques.

Findings

New methods for deriving model invariants

Framework based on string function vector spaces

Potential applications to related models

Abstract

We will outline novel approaches to derive model invariants for hidden Markov and related models. These approaches are based on a theoretical framework that arises from viewing random processes as elements of the vector space of string functions. Theorems available from that framework then give rise to novel ideas to obtain model invariants for hidden Markov and related models.