Machine learning approximations of Bayesian models

Pavone, A.; Svensson, J.; Langenberg, A.; Kwak, S.; Hoefel, U.; Pablant, N.; Brix, M.; Wolf, R. C.; for the Wendelstein 7-X Team Collaboration

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Machine learning approximations of Bayesian models

Pavone, A., Svensson, J., Langenberg, A., Kwak, S., Hoefel, U., Pablant, N., et al. (2019). Machine learning approximations of Bayesian models. Talk presented at DPG-Frühjahrstagung 2019 der Sektion Materie und Kosmos (SMuK). München. 2019-03-17 - 2019-03-22.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0003-1B4E-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-1C21-9

Genre: Talk

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Creators:
Pavone, A.¹, Author
Svensson, J.², Author
Langenberg, A.¹, Author
Kwak, S.², Author
Hoefel, U.¹, Author
Pablant, N.³, Author
Brix, M.³, Author
Wolf, R. C.¹, Author
for the Wendelstein 7-X Team Collaboration, Author

Affiliations:
1Stellarator Heating and Optimisation (E3), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040305
2Stellarator Dynamics and Transport (E5), Max Planck Institute for Plasma Physics, Max Planck Society, ou_2040306
3External Organizations, ou_persistent22

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Language(s): eng - English

Dates: Submitted: 2018

Publication Status: Submitted

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Title: DPG-Frühjahrstagung 2019 der Sektion Materie und Kosmos (SMuK)

Place of Event: München

Start-/End Date: 2019-03-17 - 2019-03-22

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Title: Verhandlungen der Deutschen Physikalischen Gesellschaft

Other : Verhandl. DPG

Source Genre: Journal

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Publ. Info: Bad Honnef : DPG

Pages: - Volume / Issue: (VI) 54 (2) Sequence Number: P 5.2 Start / End Page: - Identifier: CoNE: https://pure.mpg.de/cone/journals/resource/04200195