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 ...

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.

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 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 ...

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 ...

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

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill ...