Title :
Bayesian modeling: an amendment to the AI-ESTATE standard
Author :
Kaufman, Mark A. ; Sheppard, John W.
Author_Institution :
NSWC Corona Div., CA
Abstract :
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
Keywords :
IEEE standards; XML; artificial intelligence; automatic test equipment; automatic test software; belief networks; fault diagnosis; AI-ESTATE standard; Bayesian diagnosis; Bayesian model; Bayesian networks; IEEE Std 1232; XML; exchange Bayesian knowledge; support system prognosis; system diagnosis; Automatic testing; Bayesian methods; Context modeling; Corona; Electronic equipment testing; Random variables; Software standards; Standards development; Standards publication; System testing;
Conference_Titel :
Autotestcon, 2005. IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9101-2
DOI :
10.1109/AUTEST.2005.1609173
