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Joint filtering of mixed Markov processes in discrete time

S. Ya. Zhuk


An optimal and a quasi-optimal algorithm for filtering mixed Markov processes in discrete time, in which the discrete component is a Markov chain while the continuous component consists of sections of Markov sequences, are synthesized. Using the criterion of minimum a posteriori risk, a Bayes decision rule is obtained for one form of the loss function. The well-known and the quasi-optimal filtering algorithm synthesized here are compared using statistical modeling on a computer.

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