Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

Ford, Jason J., Krishnamurpthy, Vikram, & Moore, John B. (1993) Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states. In Conference on Intelligent Signal Processing and Communication Systems (ISPACS), October 1993, Sendai, Japan.

Abstract

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

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ID Code: 78150
Item Type: Conference Paper
Refereed: Yes
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 1993 [please consult the author]
Deposited On: 30 Oct 2014 23:54
Last Modified: 01 Nov 2014 04:55

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