☆ 4.7 Article

Efficient maximum likelihood parameterization of continuous-time Markov processes

JOURNAL OF CHEMICAL PHYSICS (2015)

期刊

JOURNAL OF CHEMICAL PHYSICS

卷 143, 期 3, 页码 -

出版社

AMER INST PHYSICS

DOI: 10.1063/1.4926516

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类别

Chemistry, Physical Physics, Atomic, Molecular & Chemical

资金

National Science Foundation
National Institutes of Health [NIH R01-GM62868, NIH S10 SIG 1S10RR02664701, NSF MCB-0954714]

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Protocol

Reagent

摘要

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. (C) 2015 AIP Publishing LLC.

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