Consistent HMM parameter estimation using Kerridge inaccuracy rates
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In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on Kerridge inaccuracy rate (KIR) concepts. Under mild identifiability conditions, we prove that our online KIR-based estimator is strongly consistent. In simulation studies, we illustrate the convergence behaviour of our proposed online KIR-based estimator and provide a counter-example illustrating the local convergence properties of the well known recursive maximum likelihood estimator (arguably the best existing solution).
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|Item Type:||Conference Paper|
|Keywords:||parameter estimation, Kerridge inaccuracy, hidden Markov model|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2013 Engineers Australia|
|Deposited On:||13 Jan 2014 22:45|
|Last Modified:||19 Jan 2014 04:48|
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