Using Prior Information in Bayesian Inference—with Application to Fault Diagnosis

摘要

In this paper we consider Bayesian inference using training data combined with prior information. The prior information considered is response and causality information which gives constraints on the posterior distribution. It is shown how these constraints can be expressed in terms of the prior probability distribution, and how to perform the computations. Further, it is discussed how this prior information improves the inference.