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Bayesian approach to inverse statistical mechanics

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

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.

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  • Received 27 December 2013

DOI:https://doi.org/10.1103/PhysRevE.89.052113

©2014 American Physical Society

Authors & Affiliations

Michael Habeck*

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

  • *mhabeck@gwdg.de

Article Text

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References

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Issue

Vol. 89, Iss. 5 — May 2014

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