Bayesian Inference Based on Stationary Fokker-Planck Sampling

A novel formalism for Bayesian learning in the context of complex inference models is proposed. The method is based on the estimation of the marginal posterior densities via Stationary Fokker--Planck sampling. Bayesian inference is performed in classification and regression examples with a computation cost that grows slowly with the model's dimension.