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Abstract: Markovian jump systems (MJSs) evolve in a jump-wise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded Markov chain governing the jumps. Under the assumption of a time-invariant but random TPM, an approximate recursion for the TPMs posterior probability density function (PDF) within the Bayesian... (Update)
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BibTeX entry: (Update)
@misc{ markovian-online,
author = "Probabilities For Markovian",
title = "Online Bayesian Estimation of Transition",
url = "citeseer.ist.psu.edu/764976.html" }
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