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 ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 62, No. 1 (2000), pp. 57-75 (19 pages) Hidden Markov models form an extension of mixture models which provides a ...

A new process—the factorial hidden Markov volatility (FHMV) model—is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a ...

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. These models have ...

The amino acid sequence of the transmembrane protein and its corresponding positions on the cell membrane are transformed into a hidden Markov process. After evaluating the parameters, the Viterbi ...

C. Bracken, B. Rajagopalan, & E. Zagona (2014). “A Hidden Markov Model Combined with Climate Indices for Multi-decadal Streamflow Simulation,” Water Resources Research, 50, 7836-7846. Abstract: ...

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 ...

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those ...

Would you know a bear market if you saw one? Hindsight suggests this is an easy task. The telltale signs are obvious… after the fact. From prices dropping below moving averages to negative rolling ...

Markov Models for disease progression are common in medical decision making (see references below). The parameters in a Markov model can be estimated by observing the time it takes patients in any ...