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Proceedings Paper

How to convert Bayesian causal networks into equivalent equation based data models
Author(s): Holger M. Jaenisch; James W. Handley; Nathaniel G. Albritton; Kristina L. Jaenisch; Stephen E. Moren
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Paper 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.

Paper Details

Date Published: 19 March 2009
PDF: 11 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430M (19 March 2009); doi: 10.1117/12.817867
Show Author Affiliations
Holger M. Jaenisch, Amtec Corp. (United States)
Licht Strahl Engineering Inc. (United States)
James W. Handley, Amtec Corp. (United States)
Licht Strahl Engineering Inc. (United States)
Nathaniel G. Albritton, Amtec Corp. (United States)
Kristina L. Jaenisch, Licht Strahl Engineering Inc. (United States)
Stephen E. Moren, Amtec Corp. (United States)


Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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