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  • Cited by 10
  • Print publication year: 2011
  • Online publication date: June 2011

1 - Hidden Markov processes in the context of symbolic dynamics

Summary

Abstract. In an effort to aid communication among different fields and perhaps facilitate progress on problems common to all of them, this article discusses hidden Markov processes from several viewpoints, especially that of symbolic dynamics, where they are known as sofic measures or continuous shift-commuting images of Markov measures. It provides background, describes known tools and methods, surveys some of the literature, and proposes several open problems.

Introduction

Symbolic dynamics is the study of shift (and other) transformations on spaces of infinite sequences or arrays of symbols and maps between such systems. A symbolic dynamical system, with a shift-invariant measure, corresponds to a stationary stochastic process. In the setting of information theory, such a system amounts to a collection of messages. Markov measures and hidden Markov measures, also called sofic measures, on symbolic dynamical systems have the desirable property of being determined by a finite set of data. But not all of their properties, for example the entropy, can be determined by finite algorithms. This article surveys some of the known and unknown properties of hidden Markov measures that are of special interest from the viewpoint of symbolic dynamics. To keep the article self contained, necessary background and related concepts are reviewed briefly. More can be found in [47, 56, 55, 71].

We discuss methods and tools that have been useful in the study of symbolic systems, measures supported on them, and maps between them.