Maximum a Posteriori Estimation of Coupled Hidden Markov Models

Authors:
Iead Rezek;Michael Gibbs;Stephen J. Roberts
Affiliations:
Robotics Research Group, Department of Engineering Science, University of Oxford, Oxford, UK;Robotics Research Group, Department of Engineering Science, University of Oxford, Oxford, UK;Robotics Research Group, Department of Engineering Science, University of Oxford, Oxford, UK
Venue:
Journal of VLSI Signal Processing Systems
Year:
2002

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Abstract

Coupled Hidden Markov Models (CHMM) are a tool which model interactions between variables in state space rather than observation space. Thus they may reveal coupling in cases where classical tools such as correlation fail. In this paper we derive the maximum a posteriori equations for the Expectation Maximisation algorithm. The use of the models is demonstrated on simulated data, as well as in a variety of biomedical signal analysis problems.