Optimal filtering, prediction and smoothing of hidden Markov models

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.<>