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Hidden Markov chain identification

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1 Author(s)
Verriest, E.I. ; Sch. of ECE, Georgia Inst. of Technol., Atlanta, GA, USA

An identification scheme is developed for hidden Markov models (HMM). Unlike the realization problem, where one starts from exact probabilities, the identification problem makes a statistical inference from the pathwise output sequences. The basic principle in the identification of Markovian finite state systems from nonnumeric inputs and outputs is its lifting to a numerical representation via the vector valued indicator function. This allows the subsequent use of subspace methods which generate the relevant statistics for identification.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003

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