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A sequential filtering scheme for the risk-sensitive state estimation of partially observed Markov chains is presented. The previously introduced risk-sensitive filters are unified in the context of risk-sensitive Maximum A Posterior Probability (MAP) estimation. Structural results for the filter banks are given. The influence of the availability of information and the transition probabilities on...
The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to a generalized notion of probability distribution for Markov chains. The evolution and the asymptotic behavior of this generalized or “risk-sensitive” probability distribution is studied...
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