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• W1753332021 abstract "In this paper, we study the problem of estimating a Markov chain X(signal) from its noisy partial information Y , when the transition probability kernel depends on some unknown parameters. Our goal is to compute the conditional distribution process P{Xn|Yn, . . . , Y1}, referred to hereafter as the optimal filter. We rewrite the system, so that the kernel is now known but the uncertainty is transfered to the initial conditions. We show that, under certain conditions, the optimal filter will forget any erroneous initialization. So, starting with a ‘good’ prior distribution on the parameters, the filter will ultimately choose the correct value. This can also be seen as an asymptotic stability result, for non-ergodic systems." @default.
• W1753332021 created "2016-06-24" @default.
• W1753332021 creator A5039730029 @default.
• W1753332021 date "2002-10-02" @default.
• W1753332021 modified "2023-09-27" @default.
• W1753332021 title "Asymptotic Stability of the optimal filter for non-ergodic signals" @default.
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• W1753332021 hasPublicationYear "2002" @default.
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