Bayesian inference with information content model check for Langevin equations

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

References in corpus (2)

• Inferring maps of forces inside cell membrane microdomains
• Fast Bayesian inference of optical trap stiffness and particle diffusion