Bayesian modeling: an amendment to the AI-ESTATE standard

Recent advances in diagnostic technology have resulted in the need to examine these technologies for expanding current work in diagnostic standards. Specifically, the use of Bayesian networks for system diagnosis is becoming more common, thus warranting consideration of a Bayesian modeling within IEEE Std 1232 (AI-ESTATE). In the following, we present a discussion of Bayesian diagnosis as a basis for introducing a new information model to support exchange Bayesian knowledge. We also describe a simple extension to the model to support system prognosis. Finally, we discuss recent initiatives within the IEEE to update their standard exchange mechanisms to support XML as "preferred" medium

[1]  John W. Sheppard.  Accounting for false indication in a Bayesian diagnostics framework , 2003, Proceedings AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference..

[2]  Douglas A. Schenck,et al.  Information modeling: the EXPRESS way , 1994 .

[3]  J.W. Sheppard,et al.  Bayesian diagnosis and prognosis using instrument uncertainty , 2005, IEEE Autotestcon, 2005..

[4]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[5]  John W. Sheppard,et al.  A Bayesian approach to diagnosis and prognosis using built-in test , 2005, IEEE Transactions on Instrumentation and Measurement.

[6]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[7]  John W. Sheppard,et al.  IEEE 1232 and P1522 standards , 2000, 2000 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. Future Sustainment for Military Aerospace (Cat. No.00CH37057).

[8]  Michael C. Horsch,et al.  Dynamic Bayesian networks , 1990 .