Journal article
Estimation of Complete Discrete Multivariate Probability Distributions from Scarce Data with Application to Risk Assessment and Fault Detection
Industrial & engineering chemistry research, v 53(18), pp 7538-7547
07 May 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents a method of estimating discrete multivariate probability distributions from scarce historical data. Of particular interest is the estimation of the probabilities of rare events. The method is based on maximizing the information entropy subject to equality constraints on the moments of the estimated probability distributions. Two criteria are proposed for optimal selections of the moment functions. The method models nonlinear and nonmonotonic relations with an optimal level of model complexity. Not only does it allow for the estimation of the probabilities of rare events, but, together with Bayesian networks, it also provides a framework to model fault propagation in complex highly interactive systems. An application of this work is in risk assessment and fault detection using Bayesian networks, especially when an accurate first-principles model is not available. The performance of the method is shown through an example.
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Details
- Title
- Estimation of Complete Discrete Multivariate Probability Distributions from Scarce Data with Application to Risk Assessment and Fault Detection
- Creators
- Taha Mohseni Ahooyi - Drexel UniversityJeffrey E. Arbogast - Air LiquideUlku G. Oktem - Risk#R#Management and Decision Processes Center, Wharton School, Philadelphia, Pennsylvania 19104, United StatesWarren D. Seider - University of PennsylvaniaMasoud Soroush - Drexel University
- Publication Details
- Industrial & engineering chemistry research, v 53(18), pp 7538-7547
- Publisher
- American Chemical Society; Washington, DC
- Number of pages
- 10
- Grant note
- CBET-1066461; CBET-1066475; 1066461 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000335878700024
- Scopus ID
- 2-s2.0-84900400370
- Other Identifier
- 991019168800904721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Engineering, Chemical