How to convert Bayesian causal networks into equivalent equation based data models
Holger M. Jaenisch, James W. Handley, Nathaniel G. Albritton, Kristina L. Jaenisch, Stephen E. Moren · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
We present a simple approach for deriving ensembles of training data from notional belief networks. This is accomplished by specifying the belief variable interactions in the form of Bayes expert system or directed graph, where the node conditional and prior probabilities are specified heuristically from data or from subject matter expert (SME) heuristics. The resulting network is then sampled across parameter space and the associated input/output pairs retained for deriving a principal component Data Model using regression techniques. The method is general and the details of the algorithm are presented.