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 ...

Christian P. Robert, Tobias Ryden, D. M. Titterington, Bayesian Inference in Hidden Markov Models through the Reversible Jump Markov Chain Monte Carlo Method, Journal of the Royal Statistical Society.

Pierre Ailliot, Craig Thompson, Peter Thomson, Space: Time Modelling of Precipitation by Using a Hidden Markov Model and Censored Gaussian Distributions, Journal of the Royal Statistical Society.

This paper proposes a hidden state Markov model (HMM) that incorporates workers’ unobserved labor market attachment into the analysis of labor market dynamics. Unlike previous literature, which ...

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.

“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 ...

Hidden Markov Models (HMM) One characteristic of speech problems as well as chromosome karyotyping is that the vectors can be of variable length.