Online estimation of hidden Markov models
Jörg Stiller, Günter Radons · IEEE Signal Processing Letters · 1999
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.