Bayesian Treatment of Model Uncertainty for Partially Applicable Models

Droguett, Enrique López; Mosleh, Ali

doi:10.1111/risa.12121

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…Model averaging is applicable if a finite number of practical alternative model structures are available, which is the case for many failure mechanisms relevant to electronic systems [3,4]. All these approaches potentially allow for the incorporation of actual data into the model uncertainty assessment process via the use of a framework [5]. The Monte Carlo method is a sensitivity-testing tool for slope stability and a method that can be used to calculate the probability of failure of a given earth slope [6].…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of uncertainty in physics-of-Failure analysis of microelectronics damage

Wu¹

2012

Materials Science and Engineering: A

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Section: Introductionmentioning

confidence: 99%

Assessing the impact of uncertainty in physics-of-Failure analysis of microelectronics damage

Wu¹

2012

Materials Science and Engineering: A

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“…64 Likewise, López Droguett and Mosleh designed a Bayesian mathematical model to describe model uncertainty quantitatively. 65 To cope with the uncertainties in different kinds of model interactions, Sankararaman and Mahadevan connected the properties of multiple models, their inputs, experiment data, and the sources of model errors by a network. 66 It is applied to assess the uncertainty of an entire simulation system in an integrated manner.…”

Section: The Vvanduq Methods On Model Credibility Evaluationmentioning

confidence: 99%

A pattern-based validation method for the credibility evaluation of simulation models

2019

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Measuring the credibility of a simulation model has always been challenging due to its growing uncertainty and complexity. During the past decades, plenty of metrics and evaluation procedures have been developed for evaluating different sorts of simulation models. Most of the existing research focuses on the direct comparison of numerical results with a group of reference data. However, it is sometimes unsuitable for evolving dynamic models such as the multi-agent models. With the same condition, both the practical system and the simulation model perform highly dynamic actions. The credibility of the model with insufficient information, non-stationary states and changing environment is unable to acquire through a direct pair comparison. This paper presents a pattern-based validation method to complementarily extract hidden patterns that exist in both a simulation model and its reference data, and assess the model performance in a different aspect. Firstly, multi-dimensional perceptually important points strategy is modified to find the patterns exist in time-serial data. Afterward, a pattern organizing topology is applied to automatically depict required pattern from reference data and assess the performance of the corresponding simulation model. The extensive case study on three simulation models shows the effectiveness of the proposed method.

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“…However, model applicability represents the degree to which the model is suitable for the specific situation and problem (represented by the conceptualization and intended use function attributes). 36 A synthetic review is presented in Table A.1 in Supplemental Appendix A.…”

Section: Assessing the Trustworthiness And Credibility Of Risk Assessmentioning

confidence: 99%

“…35 In particular, for cases in which no model exists to address the particular problem of interest, and the analysis relies mainly on the subjective assumptions that the model is partially applicable to the problem, two main attributes define model uncertainty: model Credibility and model Applicability . 36 Model credibility refers to the quality of the model in estimating the unknown in its intended domain of application and is defined by a set of attributes related to the model-building process and utilization procedure ( conceptualization and implementation , which are in turn broken down into other sub-attributes). However, model applicability represents the degree to which the model is suitable for the specific situation and problem (represented by the conceptualization and intended use function attributes).…”

Section: Assessing the Trustworthiness And Credibility Of Risk Assessmentioning

confidence: 99%

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

Bani-Mustafa

Pedroni

Zio

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Risk assessment provides information to support decision-making. Then, the confidence that can be put in its outcomes is fundamental, and this depends on the accuracy, representativeness and completeness of the models used in the risk assessment. A quantitative measure is needed to assess the credibility and trustworthiness of the outcomes obtained from such models, for decision-making purposes. This article proposes a four-level, top-down, hierarchical tree to identify the main attributes and criteria that affect the level of trustworthiness of models used in risk assessment. The level of trustworthiness (Level 1) is broken down into two attributes (Level 2), three sub-attributes and one “leaf” attribute (Level 3), and seven basic “leaf” sub-attributes (Level 4). Based on this hierarchical decomposition, a bottom-up, quantitative approach is employed for the assessment of model trustworthiness, using tangible information and data available for the basic “leaf” sub-attributes (Level 4). Analytical hierarchical process is adopted for evaluating and aggregating the sub-attributes, and Dempster–Shafer theory is adopted to consider the uncertainty and the inconsistency in the experts’ judgments. The approach is applied to a case study concerning the modeling of the residual heat removal system of a nuclear power plant, to compute its failure probability. The relative trustworthiness of two mathematical models of different complexity is evaluated: a fault tree and a multi-states physics-based model. The trustworthiness of the multi-states physics-based model is found to outweigh that of the fault tree model, which can be explained by the fact that multi-states physics-based model takes into account the components failure dependency relations and degradation effects. The feasibility and reasonableness of the approach are, thus, demonstrated, paving the way for its potential applicability to inform decision-making on safety-critical systems.

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Bayesian Treatment of Model Uncertainty for Partially Applicable Models

Cited by 11 publications

References 35 publications

Assessing the impact of uncertainty in physics-of-Failure analysis of microelectronics damage

Assessing the impact of uncertainty in physics-of-Failure analysis of microelectronics damage

A pattern-based validation method for the credibility evaluation of simulation models

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

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