Abstract Hidden Markov models (HMMs) and related models have become standard in
statistics during the last 15--20 years, with applications in diverse areas like speech and …

Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden
Markov model (HMM) parameters from their posterior distribution given observed data …

The parameters of a hidden Markov model (HMM) can be estimated by numerical
maximization of the log-likelihood function or, more popularly, using the expectation …

Hidden Markov models (HMMs) represent a very important tool for analysis of signals and
systems. In the past two decades, HMMs have attracted the attention of various research …

Sequence analysis is being more and more widely used for the analysis of social sequences
and other multivariate categorical time series data. However, it is often complex to describe …

Hidden Markov models (HMMs) have during the last decade become a widespread tool for
modelling sequences of dependent random variables. In this paper we consider a recursive …

The EM procedure is a principal tool for parameter estimation in the hidden Markov models.
However, applications replace EM by Viterbi extraction, or training (VT). VT is …

Fast estimation algorithms for Hidden Markov models (HMMs) for given data are presented.
These algorithms start from the alpha-EM algorithm which includes the traditional log-EM as …

Hidden Markov models (HMMs) have during the last decade become a widely spread tool
for modelling sequences of dependent random variables. Inference for HMMs has been …

Online (also called “recursive” or “adaptive”) estimation of fixed model parameters in hidden
Markov models is a topic of much interest in times series modeling. In this work, we propose …