Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Building large-scale Bayesian networks
The Knowledge Engineering Review
Learning Bayesian Networks
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
The New Six Sigma: A Leader's Guide to Achieving Rapid Business Improvement and Sustainable Results
The New Six Sigma: A Leader's Guide to Achieving Rapid Business Improvement and Sustainable Results
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
On the effectiveness of early life cycle defect prediction with Bayesian Nets
Empirical Software Engineering
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Risk-averse production planning
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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This paper presents a statistical approach to quantitatively measure the current exposure of a company to failures and defects in product quality or to compliance to government regulations. This approach is based on causal networks, which have previously been applied to other fields, such as systems maintenance and reliability. Causal networks allow analysts to causally explain the values of variables (an explanatory approach), to assess the effect of interventions on the structure of the data-generating process, and to evaluate Bwhat-if[ scenarios, that is, alternative methods or policies (an exploratory approach). Building the causal structure raises some challenges. In particular, there is no automated way to collect the needed data. We present a methodology for model selection and probability elicitation based on expert knowledge. We apply the proposed approach to the case of pharmaceutical manufacturing processes. The use of such networks allows for a more rigorous comparison of practices across different manufacturing sites, creates the opportunity for risk remediation, and allows us to evaluate alternative methods and approaches.