How to convert Bayesian causal networks into equivalent equation based data models

Holger M. Jaenisch; James W. Handley; Nathaniel G. Albritton; Kristina L. Jaenisch R.N.; Stephen E. Moren

doi:10.1117/12.817867

19 March 2009 How to convert Bayesian causal networks into equivalent equation based data models

Holger M. Jaenisch, James W. Handley, Nathaniel G. Albritton, Kristina L. Jaenisch R.N., Stephen E. Moren

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 73430M (2009) https://doi.org/10.1117/12.817867
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Holger M. Jaenisch, James W. Handley, Nathaniel G. Albritton, Kristina L. Jaenisch R.N., and Stephen E. Moren "How to convert Bayesian causal networks into equivalent equation based data models", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430M (19 March 2009); https://doi.org/10.1117/12.817867

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