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连续时间马尔可夫过程的高效最大似然参数化

Efficient maximum likelihood parameterization of continuous-time Markov processes.

作者信息

McGibbon Robert T, Pande Vijay S

机构信息

Department of Chemistry, Stanford University, Stanford, California 94305, USA.

出版信息

J Chem Phys. 2015 Jul 21;143(3):034109. doi: 10.1063/1.4926516.

Abstract

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.

摘要

有限状态空间上的连续时间马尔可夫过程在自然科学和社会科学的许多领域中被广泛用于对动态过程进行建模。在此,我们引入一种极大似然估计器,用于根据在有限时间间隔内观测到的数据构建此类模型。该估计器比先前的方法效率大幅提高,能够计算所有模型参数的确定性置信区间,并且可以轻松地对模型施加重要的物理约束,如细致平衡。我们展示并讨论了这些模型相对于现有离散时间马尔可夫模型在分子动力学模拟分析方面的优势。

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