 Computing Sobol indices in probabilistic graphical modelsRafael Ballester-Ripoll and Manuele Leonelli Reliability Engineering and System Safety, 2022, vol. 225, issue C Abstract: We show how to apply Sobol’s method of global sensitivity analysis to measure the influence exerted by a set of nodes’ evidence on a quantity of interest expressed by a Bayesian network. Our method exploits the network structure so as to transform the problem of Sobol index estimation into that of marginalization inference and, unlike Monte Carlo based estimators for variance-based sensitivity analysis, it gives exact results when exact inference is used. Moreover, the method supports the case of correlated inputs and it is efficient as long as eliminating the inputs’ ancestors is computationally affordable. The proposed algorithms are inspired by the field of tensor networks and generalize earlier tensor sensitivity techniques from the acyclic to the cyclic case. We demonstrate our method on three medium to large Bayesian networks in the areas of structural reliability and project risk management. Keywords: Global sensitivity analysis; Bayesian networks; Sobol indices; Uncertainty quantification; Tensor networks (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002204 DOI: 10.1016/j.ress.2022.108573 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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