Hidden Markov Model (HMM): A model for sequential data where the system is assumed to follow a Markov process with hidden states producing observed outcomes.

Hidden Markov models (HMMs) provide a robust statistical framework for analysing sequential data by assuming that the observed processes are driven by underlying, unobserved states.

Hidden Markov models (HMMs) are a useful tool for capturing the behavior of overdispersed, autocorrelated data. These models have been applied to many different problems, including speech recognition, ...

T. Rolf Turner, Murray A. Cameron, Peter J. Thomson, Hidden Markov Chains in Generalized Linear Models, The Canadian Journal of Statistics / La Revue Canadienne de ...

Definition A Hidden Markov Model (HMM) is a statistical model that assumes there are underlying, unobservable (hidden) states that drive observable outcomes.

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 ...

Details of the model, which deals with losses of more than $1 million, were published in a 2010 paper in the Journal of Banking & Finance. Scaling formula Given that individual banks do not typically ...

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 ...