DocumentCode
1528612
Title
Online estimation of hidden Markov models
Author
Stiller, J.C. ; Radons, G.
Author_Institution
Inst. fur Theor. Phys., Kiel Univ., Germany
Volume
6
Issue
8
fYear
1999
Firstpage
213
Lastpage
215
Abstract
We present a novel and simple online estimation algorithm for hidden Markov models, with memory requirements independent of the data length. The transition matrices and the state distribution are obtained at any instant as contractions of tensorial quantities, which are iteratively reestimated.
Keywords
convergence of numerical methods; hidden Markov models; matrix algebra; parameter estimation; probability; HMM; adaptive algorithm; contraction operation; convergence; data length; hidden Markov models; iterative reestimation; memory requirements; online estimation algorithm; state distribution; tensorial quantities; transition matrices; transition probability; Biological control systems; Biological system modeling; Communication system control; Data analysis; Hidden Markov models; Image sequences; Iterative algorithms; Recursive estimation; Speech recognition; State estimation;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/97.774870
Filename
774870
Link To Document