Unified Treatment of Hidden Markov Switching Models

TL;DR

This paper provides a unified framework for hidden Markov switching models, integrating existing approaches, introducing new models, and simplifying the understanding of regime-switching time-series analysis.

Contribution

It offers a comprehensive overview of hidden Markov switching models, unifies various existing models, and introduces new models and inference methods within this framework.

Findings

Unified graphical model framework for switching models

Simplified and comprehensive overview of existing models

New models and inference routines derived from the unified approach

Abstract

Many real-world problems encountered in several disciplines deal with the modeling of time-series containing different underlying dynamical regimes, for which probabilistic approaches are very often employed. In this paper we describe several such approaches in the common framework of graphical models. We give a unified overview of models previously introduced in the literature, which is simpler and more comprehensive than previous descriptions and enables us to highlight commonalities and differences among models that were not observed in the past. In addition, we present several new models and inference routines, which are naturally derived within this unified viewpoint.