Journal ArticleDOI
Validation of reliability computational models using Bayes networks
Sankaran Mahadevan,Ramesh Rebba +1 more
TLDR
The methodology includes uncertainty in the experimental measurement, and the posterior and prior distributions of the model output are used to compute a validation metric based on Bayesian hypothesis testing.About:
This article is published in Reliability Engineering & System Safety.The article was published on 2005-02-01. It has received 197 citations till now. The article focuses on the topics: Verification and validation of computer simulation models & Bayesian statistics.read more
Citations
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Journal ArticleDOI
Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.
Journal ArticleDOI
Calibration, validation, and sensitivity analysis: What's what
TL;DR: Some technical challenges that must be resolved for successful validation of a predictive modeling capability are identified and a formal description of a “model discrepancy” term is identified.
Journal ArticleDOI
Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA
Zaili Yang,S. Bonsall,Jin Wang +2 more
TL;DR: A novel, efficient fuzzy rule-based Bayesian reasoning approach for prioritizing failures in failure mode and effects analysis (FMEA) and is specifically intended to deal with some of the drawbacks concerning the use of conventional fuzzy logic methods in FMEA.
ReportDOI
Verification and validation benchmarks
TL;DR: In this article, the authors present guidelines for the design and use of validation benchmarks, highlighting the careful design of building-block experiments, the estimation of experimental measurement uncertainty for both inputs and outputs to the code, validation metrics, and the role of model calibration in validation.
References
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Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
Book
Bayesian networks and decision graphs
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Journal ArticleDOI
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.