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Abstract: Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations. (Update)
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BibTeX entry: (Update)
A. Doucet, A. Logothetis and V. Krishnamurthy, "Stochastic sampling algorithms for state estimation of jump Markov linear systems", IEEE Trans. Automatic Control, to appear Jan. 2000. http://citeseer.ist.psu.edu/doucet00stochastic.html More
@misc{ doucet00stochastic,
author = "A. Doucet and A. Logothetis and V. Krishnamurthy",
title = "Stochastic sampling algorithms for state estimation of jump Markov linear
systems",
text = "A. Doucet, A. Logothetis and V. Krishnamurthy, Stochastic sampling algorithms
for state estimation of jump Markov linear systems, IEEE Trans. Automatic
Control, to appear Jan. 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/doucet00stochastic.html" }
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