Efficient maximum likelihood parameterization of continuous-time Markov processes

Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce a maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is dramatic...

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Bibliographic Details
Published inThe Journal of chemical physics Vol. 143; no. 3; p. 034109
Main Authors McGibbon, Robert T, Pande, Vijay S
Format Journal Article
LanguageEnglish
Published United States 21.07.2015
Subjects
Likelihood Functions
Markov Chains
Molecular Dynamics Simulation
Protein Folding
Time Factors
Uncertainty
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Summary:Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce a maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is dramatically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance. We demonstrate and discuss the advantages of these models over existing discrete-time Markov models for the analysis of molecular dynamics simulations.
ISSN:1089-7690
DOI:10.1063/1.4926516