Bayesian inference with information content model check for Langevin equations
arXiv:1708.03664 · doi:10.1103/PhysRevE.96.062106
Abstract
The Bayesian data analysis framework has been proven to be a systematic and effective method of parameter inference and model selection for stochastic processes. In this work we introduce an information content model check which may serve as a goodness-of-fit, like the chi-square procedure, to complement conventional Bayesian analysis. We demonstrate this extended Bayesian framework on a system of Langevin equations, where coordinate dependent mobilities and measurement noise hinder the normal mean squared displacement approach.
10 pages, 7 figures, REVTeX, minor revisions
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