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

Multiple-prediction-horizon recursive identification of hidden Markov models

This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard metho...

On-line Estimation and Identification of HMMs with Grouped State Values

This paper presents a signal processing scheme for the class of lumpable, or weakly-lumpable, hidden Markov models (HMMs) which have state values clustered into groups. Attention is focussed not only on state estimation for known models, but also on on-line model identification. The approach taken employs a new technique whereby separate state estimators are used for each group of state-values....

A new maximum likelihood gradient algorithm for on-line hidden Markov model identification

This paper presents a new algorithm for on-line identification of hidden Markov model (HMM) parameters. The scheme is gradient based, and provides parameter estimates which recursively maximise the likelihood function. It is therefore a recursive maximum likelihood(RML) algorithm, and it has optimal asymptotic properties. The only current on-line HMM identification algorithm with anything other...

On - Line Identi cation of Hidden Markov Models via RecursivePrediction Error Techniques 1 IAIN

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionall...