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  Optimal Experimental Design with Bayesian Parameter Identification for Chemical Reaction Networks

Rätze, K., McBride, K., & Sundmacher, K. (2020). Optimal Experimental Design with Bayesian Parameter Identification for Chemical Reaction Networks. Poster presented at 10. ProcessNet-Jahrestagung und 34. DECHEMA-Jahrestagung der Biotechnologen 2020: Processes for Future, virtual.

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 Creators:
Rätze, Karsten1, 2, Author              
McBride, Kevin3, Author              
Sundmacher, Kai2, 3, Author              
Affiliations:
1International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738143              
2Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              
3Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151              

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Language(s): eng - English
 Dates: 2020
 Publication Status: Not specified
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: -
 Degree: -

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Title: 10. ProcessNet-Jahrestagung und 34. DECHEMA-Jahrestagung der Biotechnologen 2020: Processes for Future
Place of Event: virtual
Start-/End Date: 2020-09-21 - 2020-09-24

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