Unpublished conference/Abstract (Scientific congresses, symposiums and conference proceedings)
Using optimal transport to assess the impact of prior choice on Bayesian parameter inference in dynamical systems
MINGO NDIWAGO, Damian; LEY, Christophe; HALE, Jack
202316th International Conference of the ERCIM WG on Computational and Methodological Statistics and 17th International Conference on Computational and Financial Econometrics
 

Files


Full Text
CMStatistics2023.pdf
Author postprint (91.22 kB) Creative Commons License - Attribution, ShareAlike
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
Optimal transport; prior distribution; Wasserstein Impact Measure; time-series models; ODEs
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Environmental sciences & ecology
Mathematics
Author, co-author :
MINGO NDIWAGO, Damian  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
LEY, Christophe ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
HALE, Jack  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
no
Language :
English
Title :
Using optimal transport to assess the impact of prior choice on Bayesian parameter inference in dynamical systems
Publication date :
15 December 2023
Event name :
16th International Conference of the ERCIM WG on Computational and Methodological Statistics and 17th International Conference on Computational and Financial Econometrics
Event organizer :
CFEnetwork-CMStatistics
Event place :
Berlin, Germany
Event date :
From 15 to 18 December 2023
Audience :
International
Focus Area :
Computational Sciences
FnR Project :
FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian
Funders :
FNR - Fonds National de la Recherche
Funding text :
This works is funded through the Doctoral Training Unit , Data-driven computational modelling and applications (DRIVEN) by the Luxembourg National Research Fund under the PRIDE programme (PRIDE17/12252781).
Available on ORBilu :
since 18 January 2024

Statistics


Number of views
159 (20 by Unilu)
Number of downloads
57 (4 by Unilu)

Bibliography


Similar publications



Contact ORBilu