Online identification of hidden Markov models via recursive prediction error techniques

Iain B. Collings, Vikram Krishnamurthy, John B. Moore

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


In this correspondence, an Online state and parameter identification scheme for hidden Markov models (HMM's) 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.

Original languageEnglish
Pages (from-to)3535-3539
Number of pages5
JournalIEEE Transactions on Signal Processing
Issue number12
Publication statusPublished - 1994
Externally publishedYes

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