On-line identification of hidden Markov models via recursive prediction error techniques

TitleOn-line identification of hidden Markov models via recursive prediction error techniques
Publication TypeJournal Article
Year of Publication1994
AuthorsCollings, I. B., V. Krishnamurthy, and J. B. Moore
JournalSignal Processing, IEEE Transactions on
Volume42
Pagination3535 -3539
Date Publisheddec.
ISSN1053-587X
Keywordsdiscrete state values, error analysis, finite-discrete set, hidden Markov models, HMM, initializations, Markov chain, noise, noise density, observations, on-line identification, parameter estimation, parameter identification, prediction theory, probability, recursive estimation, recursive Kullback-Leibler algorithm, recursive prediction error techniques, signal model, signal processing, simulation studies, transition probabilities
Abstract

An on-line state and parameter identification scheme for hidden Markov models (HMMs) with states in a finite-discrete set is developed using recursive prediction error (RPE) techniques. The parameters of interest are the transition probabilities and discrete state values of a Markov chain. The noise density associated with the observations can also be estimated. Implementation aspects of the proposed algorithms are discussed, and simulation studies are presented to show that the algorithms converge for a wide variety of initializations. In addition, an improved version of an earlier proposed scheme (the Recursive Kullback-Leibler (RKL) algorithm) is presented with a parameterization that ensures positivity of transition probability estimates

URLhttp://dx.doi.org/10.1109/78.340791
DOI10.1109/78.340791

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