Title :
Optimal filtering, prediction and smoothing of hidden Markov models
Author :
Li, Zbeng ; Evans, R.J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
Abstract :
Optimal filtering, prediction and smoothing algorithms for hidden Markov models (HMMs) are presented. Employing a dynamic state space description of the probability evolution for an HMM, the close structural similarity with Kalman filtering, prediction and smoothing is shown. The HMM estimation algorithms are simpler than those of the Kalman filter
Keywords :
Kalman filters; filtering and prediction theory; hidden Markov models; probability; HMM; Kalman filtering; dynamic state space description; hidden Markov models; optimal filtering; optimal prediction; optimal smoothing; probability evolution; Automatic control; Filtering algorithms; Frequency estimation; Hidden Markov models; Kalman filters; Prediction algorithms; Smoothing methods; Speech recognition; State-space methods; Viterbi algorithm;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371027
