Recursive algorithms for estimation of hidden Markov models and autoregressive models with Markov regime

Authors:
V. Krishnamurthy;George Gang Yin
Affiliations:
Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Vic.;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 7

Estimation from lossy sensor data: jump linear modeling and Kalman filtering

Proceedings of the 3rd international symposium on Information processing in sensor networks
Asymptotic properties of nonlinear autoregressive Markov processes with state-dependent switching

Journal of Multivariate Analysis
A dynamical games approach to transmission-rate adaptation in multimedia WLAN

IEEE Transactions on Signal Processing
Analyticity, convergence, and convergence rate of recursive maximum-likelihood estimation in hidden Markov models

IEEE Transactions on Information Theory
Decentralized activation in sensor networks - global games and adaptive filtering games

Digital Signal Processing
An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems

Automatica (Journal of IFAC)
A survey of techniques for incremental learning of HMM parameters

Information Sciences: an International Journal

Quantified Score

Hi-index	754.90

Visualization

Abstract

This paper is concerned with recursive algorithms for the estimation of hidden Markov models (HMMs) and autoregressive (AR) models under the Markov regime. Convergence and rate of convergence results are derived. Acceleration of convergence by averaging of the iterates and the observations are treated. Finally, constant step-size tracking algorithms are presented and examined