Risk-sensitive maximum likelihood sequence estimation
Robert James Elliott, John B. Moore, Subhrakanti Dey · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 1996
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 state models.