Title :
Online estimation of hidden Markov models
Author :
Stiller, J.C. ; Radons, G.
Author_Institution :
Inst. fur Theor. Phys., Kiel Univ., Germany
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;
Journal_Title :
Signal Processing Letters, IEEE
