Title :
A framework for mixed estimation of hidden Markov models
Author :
Dey, Subhrakanti ; Marcus, Steven I.
Author_Institution :
Inst. for Syst. Res., Maryland Univ., College Park, MD, USA
Abstract :
In this paper, we present a framework for a mixed estimation scheme for hidden Markov models (HMM). A robust estimation scheme is first presented using the minimax method that minimizes a worst case cost for HMMs with bounded uncertainties. Then we present a mixed estimation scheme that minimizes a risk-neutral cost with a constraint on the worst-case cost. Some simulation results are also presented to compare these different estimation schemes in cases of uncertainties in the noise model
Keywords :
estimation theory; hidden Markov models; minimax techniques; probability; state estimation; bounded uncertainty; hidden Markov models; minimax method; probability; robust estimation; state estimation; worst case cost; Biomedical signal processing; Costs; Educational institutions; Hidden Markov models; Noise robustness; Probability; Signal processing algorithms; State estimation; Stochastic resonance; Uncertainty;
Conference_Titel :
Decision and Control, 1998. Proceedings of the 37th IEEE Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
0-7803-4394-8
DOI :
10.1109/CDC.1998.758243
