title: Bayesian approach to inverse statistical mechanics.
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.
authors.affiliation: Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstrasse 7, 37077 Göttingen, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Habeck
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical