Finite Horizon Robust State Estimation for Uncertain Finite-Alphabet Hidden Markov Models with Conditional Relative Entropy Constraints
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 estimation. 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 estimators are studied.