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

Date

2006-10-01

Author

Orguner, Umut

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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.

Subject Keywords

Control and Systems Engineering, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/41934

Journal

AUTOMATICA

DOI

https://doi.org/10.1016/j.automatica.2006.05.002

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

U. Orguner, “An online sequential algorithm for the estimation of transition probabilities forjump Markov linear systems,” AUTOMATICA, pp. 1735–1744, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41934.