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PUBLICATIONS

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

Published

Author(s)

Saideep Nannapaneni, Anantha Narayanan Narayanan, Ronay Ak, David Lechevalier, Thurston Sexton, Sankaran Mahadevan, Yung-Tsun Lee

Abstract

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 paper 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. The BN PMML representation is available in PMML v4.3 released by the Data Mining Group. We demonstrate the conversion of analytical models into the BN PMML representation, and the PMML representation of such models into analytical models, through a Python parser. The BNs obtained after parsing PMML representation can then be used to perform Bayesian inference. Finally, we illustrate the BN PMML schema using a real-world manufacturing case study.
Citation
The ASTM Journal of Smart and Sustainable Manufacturing
Volume
2
Pub Type
Journals

Download Paper

https://doi.org/10.1520/SSMS20180018
Local Download

Keywords

Bayesian Networks, Standards, PMML, Data analytics
Standards, Manufacturing systems design and analysis and Data and informatics

Citation

Nannapaneni, S. , Narayanan, A. , Ak, R. , Lechevalier, D. , Sexton, T. , Mahadevan, S. and Lee, Y. (2018), Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing, The ASTM Journal of Smart and Sustainable Manufacturing, [online], https://doi.org/10.1520/SSMS20180018, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924924 (Accessed April 23, 2024)

Created October 1, 2018, Updated October 12, 2021