Finite mixture models and hidden Markov models (HMMs) occupy central roles in modern statistical inference and data analysis. Finite mixture models assume that data originate from a latent ...

Markov Chains are fundamental stochastic models in which the probability of transitioning to a future state depends solely on the present state, not on the sequence of events that preceded it ...

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 an online ...

A natural way to address these issues is to combine clustering and longitudinal analyses using a hidden Markov model. We fit hidden Markov models to longitudinal data using Bayesian methods that ...

“A Hidden Markov Model Combined with Climate Indices for Multi-decadal Streamflow Simulation,” Water Resources Research, 50, 7836-7846. Abstract: Hydroclimate time series often exhibit very low ...

Abstract Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot.