Reference : Using Bayes factors to compare dynamical models of hydrological systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Life sciences : Environmental sciences & ecology Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography Physical, chemical, mathematical & earth Sciences : Mathematics Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences; Sustainable Development
	To cite this reference:	http://hdl.handle.net/10993/51573

Title :	Using Bayes factors to compare dynamical models of hydrological systems
Language :	English
Author, co-author :	Mingo Ndiwago, Damian [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
	Nijzink, Remko [Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg > Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN)]
	Ley, Christophe [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
	Schymanski, Stanislaus [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > >]
	Hale, Jack [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	5-Jun-2022
Number of pages :	1
Peer reviewed :	No
On invitation :	Yes
Audience :	International
Event name :	5th International Conference on Econometrics and Statistics (EcoSta 2022)
Event date :	4-6 June 2022
Event organizer :	Co-organized by the Working Group on Computational and Methodological Statistics (CMStatistics), the network of Computational and Financial Econometrics (CFENetwork), and the Ryukoku University.
Event place (city) :	Kyoto
Event country :	Japan
Keywords :	[en] Bayes factor ; Thermodynamic integration ; , Rainfall-runoff models ; Hydrology
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/51573
FnR project :	FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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	Fulltext file(s): File Commentary Version Size Access Open access EcoSta22.pdf Abstract Author preprint 69.41 kB View/Open

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