Abstract
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 estima- tion. Structural results for the filter banks are given. The influence of the availability of information and the transition probabilities on the decision regions and the behavior of risk-sensitive estima- tors are studied. Index Terms—Hidden Markov models (HMMs), risk-sensitive estimation.
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