Title :
Reduced-complexity smoothing for hidden Markov models
Author :
Shue, L. ; Dey, Subhrakanti
Author_Institution :
Centre for Signal Process., Nanyang Technol. Inst., Singapore
Abstract :
We investigate approximate smoothing schemes for a class of hidden Markov models (HMM), namely, HMMs with underlying Markov chains that are nearly completely decomposable. The objective is to obtain substantial computational savings. Our algorithm can not only be used to obtain aggregate smoothed estimates, but can be used to obtain systematically approximate full-order smoothed estimates with computational savings, unlike many of the aggregation methods proposed earlier
Keywords :
hidden Markov models; matrix algebra; probability; smoothing methods; state estimation; Markov chains; aggregate smoothed estimates; approximate full-order smoothed estimates; approximate smoothing schemes; computational savings; reduced-complexity smoothing; Aggregates; Control systems; Engineering management; Environmental management; Hidden Markov models; Iterative methods; Signal processing algorithms; Smoothing methods; State estimation; Steady-state;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2001.914669
