Bayesian approach to inverse statistical mechanics

    Phys. Rev. E 89, 052113 – Published 9 May 2014
    Michael Habeck

    Abstract

    Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

    DOI: http://dx.doi.org/10.1103/PhysRevE.89.052113

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    • Published 9 May 2014
    • Received 27 December 2013

    ©2014 American Physical Society

    Authors & Affiliations

    Michael Habeck*

    • Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstrasse 7, 37077 Göttingen, Germany

    • *mhabeck@gwdg.de

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