0000000000338096

AUTHOR

Thurston Sexton

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Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

2018

International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…

0209 industrial biotechnologyDesignComputer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreBayesian inferenceIndustrial and Manufacturing EngineeringArticle[SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineeringanalyticsUncertainty quantificationMonte-Carlouncertaintycomputer.programming_languageParsingBayesian networkInformationSystems_DATABASEMANAGEMENTstandardPython (programming language)XMLComputer Science ApplicationsmanufacturingComputingMethodologies_PATTERNRECOGNITIONBayesian networksControl and Systems EngineeringSurface-RoughnessData analysisPredictive Model Markup Language020201 artificial intelligence & image processingData miningcomputerXML
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