Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

Sascha Ranftl; Gian Marco Melito; Vahid Badeli; Alice Reinbacher-Köstinger; Katrin Ellermann; Wolfgang von der Linden

doi:10.3390/e22010058

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

Sascha Ranftl, Gian Marco Melito, Vahid Badeli, Alice Reinbacher-Köstinger, Katrin Ellermann, Wolfgang von der Linden

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

Original language	Undefined/Unknown
Journal	Entropy
DOIs	https://doi.org/10.3390/e22010058
Publication status	Published - 2019

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title = "Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection",

author = "Sascha Ranftl and Melito, {Gian Marco} and Vahid Badeli and Alice Reinbacher-K{\"o}stinger and Katrin Ellermann and Linden, {Wolfgang von der}",

year = "2019",

doi = "10.3390/e22010058",

language = "undefiniert/unbekannt",

journal = "Entropy",

issn = "1099-4300",

publisher = "MDPI AG",

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TY - JOUR

T1 - Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

AU - Ranftl, Sascha

AU - Melito, Gian Marco

AU - Badeli, Vahid

AU - Reinbacher-Köstinger, Alice

AU - Ellermann, Katrin

AU - Linden, Wolfgang von der

PY - 2019

Y1 - 2019

UR - https://www.mdpi.com/1099-4300/22/1/58

U2 - 10.3390/e22010058

DO - 10.3390/e22010058

M3 - Artikel

SN - 1099-4300

JO - Entropy

JF - Entropy

ER -

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

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