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

Authors:
Umut Orguner;MüBeccel Demirekler
Affiliations:
Department of Electrical and Electronics Engineering, Middle East Technical University, 06531 Ankara, Turkey;Department of Electrical and Electronics Engineering, Middle East Technical University, 06531 Ankara, Turkey
Venue:
Automatica (Journal of IFAC)
Year:
2006

Citing 13
Cited 0

System identification: theory for the user

System identification: theory for the user
Adaptive algorithms and stochastic approximations

Adaptive algorithms and stochastic approximations
Optimal filtering of discrete-time hybrid systems

Journal of Optimization Theory and Applications
Learning Automata and Stochastic Optimization

Learning Automata and Stochastic Optimization
Estimation of Markovian Jump Systems with Unknown Transition Probabilities through Bayesian Sampling

NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
Adaptive estimation of HMM transition probabilities

IEEE Transactions on Signal Processing
On-line estimation of hidden Markov model parameters based on theKullback-Leibler information measure

IEEE Transactions on Signal Processing
An improvement to the interacting multiple model (IMM) algorithm

IEEE Transactions on Signal Processing
Online Bayesian estimation of transition probabilities for Markovian jump systems

IEEE Transactions on Signal Processing
On-line identification of hidden Markov models via recursiveprediction error techniques

IEEE Transactions on Signal Processing
Expectation maximization algorithms for MAP estimation of jumpMarkov linear systems

IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems

IEEE Transactions on Signal Processing
Recursive algorithms for estimation of hidden Markov models and autoregressive models with Markov regime

IEEE Transactions on Information Theory

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper describes a new method to estimate the transition probabilities associated with a jump Markov linear system. The new algorithm uses stochastic approximation type recursions to minimize the Kullback-Leibler divergence between the likelihood function of the transition probabilities and the true likelihood function. Since the calculation of the likelihood function of the transition probabilities is impossible, an incomplete data paradigm, which has been previously applied to a similar problem for hidden Markov models, is used. The algorithm differs from the existing algorithms in that it assumes that the transition probabilities are deterministic quantities whereas the existing approaches consider them to be random variables with prior distributions.