Name
Title | ||
|---|---|---|
An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.automatica.2006.05.002 | Automatica |
Keywords | Field | DocType |
Jump Markov linear systems,Kullback–Leibler distance measure,Transition probability,Stochastic approximation | Markov process,Likelihood function,Forward algorithm,Markov chain,Algorithm,Sequential estimation,Prior probability,Hidden Markov model,Jump process,Mathematics | Journal |
Volume | Issue | ISSN |
42 | 10 | Automatica |
Citations | PageRank | References |
6 | 0.52 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Orguner | 1 | 548 | 40.11 |
| Mübeccel Demirekler | 2 | 152 | 19.39 |