Elliott, R. J. and Moore, John B. and Dey, Subhrakanti
(1996)
Risk-sensitive Maximum Likelihood sequence estimation.
IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 43 (9).
Abstract
In this brief, we consider risk-sensitive Maximum Likelihood sequence estimation for hidden Markov models with finite-discrete states. An algorithm is proposed which is essentially a risk-sensitive variation of the Viterbi algorithm. Simulation studies show that the risk-sensitive algorithm is more robust to uncertainties in the transition probability matrix of the Markov chain. Similar estimation results are also obtained for continuous-range models.
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