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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

AN EFFICIENT COMPUTATIONAL METHOD FOR PARAMETER IDENTIFICATION IN THE CONTEXT OF RANDOM SET THEORY VIA BAYESIAN INVERSION

Volume 11, Issue 4, 2021, pp. 1-18
DOI: 10.1615/Int.J.UncertaintyQuantification.2020031869
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ABSTRACT

This work deals with parameter identification problems in which uncertainties are modeled using random sets (RS), i.e., set-valued random variables. Dempster's rule of combination is applied for replacing the role of Bayes' rule to infer the posterior, which is also a RS. The considered framework allows accounting for mixed epistemic-aleatory uncertainty descriptions such as probability boxes and intervals. In this paper, we aim at an efficient computational method to sample the posterior RS using stochastic methods developed for Bayesian inverse problems. To this end, by applying the capacity transformation method, the considered problem is translated into a Bayesian inverse problem, and the region at which the posterior RS concentrates is exploited using a Markov Chain Monte Carlo (MCMC) algorithm. To sample the posterior RS, we approximate it as a random finite set whose domain consists of points obtained from the MCMC algorithm. Because the forward model could be computationally expensive and is required to be evaluated at many points, we construct a polynomial chaos expansion-based surrogate model for it. The developed approach is demonstrated with a numerical example in which measurement errors are noisy and also contain unknown but bounded biases.

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