Hidden Markov Models and Their Applications Publication Trend The graph below shows the total number of publications each year in Hidden Markov Models and Their Applications.

Markov chains provide a fundamental framework for modelling stochastic processes, where the next state depends solely on the current state. Hidden Markov models (HMMs) extend this framework by ...

We formulate a hidden Markov model in which the latent state space contains both pure and mixed strategies. We apply the model to data from an experiment in which human subjects repeatedly play a ...

We propose a hidden Markov model to correctly interpret the users 'product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A ...

“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.