Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric

Lechang Yang; Sifeng Bi; Matthias G.R. Faes; Matteo Broggi; Michael Beer

doi:10.1016/j.ymssp.2021.107954

Details

Original language	English
Article number	107954
Journal	Mechanical Systems and Signal Processing
Volume	162
Early online date	22 May 2021
Publication status	Published - 1 Jan 2022

Keywords

Approximate Bayesian computation, Bayesian inverse problem, Entropy, Imprecise probability, Jensen–Shannon divergence, Uncertainty quantification

ASJC Scopus subject areas

Engineering(all)
Control and Systems Engineering
Computer Science(all)
Signal Processing
Engineering(all)
Civil and Structural Engineering
Engineering(all)
Aerospace Engineering
Engineering(all)
Mechanical Engineering
Computer Science(all)
Computer Science Applications

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Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric. / Yang, Lechang; Bi, Sifeng; Faes, Matthias G.R. et al.
In: Mechanical Systems and Signal Processing, Vol. 162, 107954, 01.01.2022.

Research output: Contribution to journal › Article › Research › peer review

Yang, L, Bi, S, Faes, MGR, Broggi, M & Beer, M 2022, 'Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric', Mechanical Systems and Signal Processing, vol. 162, 107954. https://doi.org/10.1016/j.ymssp.2021.107954

Yang, L., Bi, S., Faes, M. G. R., Broggi, M., & Beer, M. (2022). Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric. Mechanical Systems and Signal Processing, 162, Article 107954. https://doi.org/10.1016/j.ymssp.2021.107954

Yang L, Bi S, Faes MGR, Broggi M, Beer M. Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric. Mechanical Systems and Signal Processing. 2022 Jan 1;162:107954. Epub 2021 May 22. doi: 10.1016/j.ymssp.2021.107954

Yang, Lechang ; Bi, Sifeng ; Faes, Matthias G.R. et al. / Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric. In: Mechanical Systems and Signal Processing. 2022 ; Vol. 162.

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author = "Lechang Yang and Sifeng Bi and Faes, {Matthias G.R.} and Matteo Broggi and Michael Beer",

note = "Funding Information: Dr. Matthias G.R. Faes acknowledges the support of the Research Foundation Flanders (FWO) in the context of his post-doctoral grant 12P3519N, as well as the Alexander von Humboldt foundation. Funding Information: Dr. Lechang Yang acknowledges the support of the National Natural Science Foundation of China under Grant 52005032, the Aeronautical Science Foundation of China under Grant 2018ZC74001, the Fundamental Research Funds for the Central Universities of China under Grant FRF-TP-20-008A2, QNXM20210024 and the China Scholarship Council (CSC) under Grant 201906465064. ",

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AU - Yang, Lechang

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AU - Faes, Matthias G.R.

AU - Broggi, Matteo

AU - Beer, Michael

N1 - Funding Information: Dr. Matthias G.R. Faes acknowledges the support of the Research Foundation Flanders (FWO) in the context of his post-doctoral grant 12P3519N, as well as the Alexander von Humboldt foundation. Funding Information: Dr. Lechang Yang acknowledges the support of the National Natural Science Foundation of China under Grant 52005032, the Aeronautical Science Foundation of China under Grant 2018ZC74001, the Fundamental Research Funds for the Central Universities of China under Grant FRF-TP-20-008A2, QNXM20210024 and the China Scholarship Council (CSC) under Grant 201906465064.

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KW - Approximate Bayesian computation

KW - Bayesian inverse problem

KW - Entropy

KW - Imprecise probability

KW - Jensen–Shannon divergence

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Research@Leibniz University

Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric

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