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

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Originalsprache	Englisch
Aufsatznummer	107954
Fachzeitschrift	Mechanical Systems and Signal Processing
Jahrgang	162
Frühes Online-Datum	22 Mai 2021
Publikationsstatus	Veröffentlicht - 1 Jan. 2022

ASJC Scopus Sachgebiete

Ingenieurwesen (insg.)
Steuerungs- und Systemtechnik
Informatik (insg.)
Signalverarbeitung
Ingenieurwesen (insg.)
Tief- und Ingenieurbau
Ingenieurwesen (insg.)
Luft- und Raumfahrttechnik
Ingenieurwesen (insg.)
Maschinenbau
Informatik (insg.)
Angewandte Informatik

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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, Jahrgang 162, 107954, 01.01.2022.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › 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, Jg. 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, Artikel 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 Mai 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 ; Jahrgang 162.

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title = "Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric",

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

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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T1 - Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric

AU - Yang, Lechang

AU - Bi, Sifeng

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.

PY - 2022/1/1

Y1 - 2022/1/1

KW - Approximate Bayesian computation

KW - Bayesian inverse problem

KW - Entropy

KW - Imprecise probability

KW - Jensen–Shannon divergence

KW - Uncertainty quantification

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U2 - 10.1016/j.ymssp.2021.107954

DO - 10.1016/j.ymssp.2021.107954

M3 - Article

AN - SCOPUS:85110273914

VL - 162

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

M1 - 107954

ER -

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

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