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A Fully Bayesian Approach For Inferring Physical Properties..:

, In: 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS),

Vono, Maxime ; Goicoechea, Javier R. ; Gratier, Pierre... - p. 1-5 , 2019

Link: https://doi.org/10.1109/WHISPERS.2019.8920859

RT 
T1 <cTitle>2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)</cTitle>: T1 <Title>A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-8920859&Exemplar=1&LAN=DE
				
A1 Vono, Maxime
A1 Goicoechea, Javier R.
A1 Gratier, Pierre
A1 Guzman, Viviana V.
A1 Hughes, Annie
A1 Kainulainen, Jouni
A1 Languignon, David
A1 Bourlot, Jacques Le
A1 Levrier, Fracois
A1 Listz, Harvey S.
A1 Oberg, Karin I.
A1 Bron, Emeric
A1 Orkisz, Jan H.
A1 Peretto, Nicolas
A1 Pety, Jerome
A1 Roueff, Antoine
A1 Roueff, Evelyne
A1 Sievers, Albrecht
A1 Magalhaes, Victor de Souza
A1 Tremblin, Pascal
A1 Chainais, Pierre
A1 Petit, Franck Le
A1 Bardeau, Sebastien
A1 Bourguignon, Sebastien
A1 Chanussot, Jocelyn
A1 Gaudel, Mathilde
A1 Gerin, Maryvonne
YR 2019
SN 2158-6276
K1 Bayes methods
K1 Chemicals
K1 Instruments
K1 Photonics
K1 Signal to noise ratio
K1 Computational modeling
K1 Inference algorithms
K1 Approximate Bayesian computation
K1 Markov chain Monte Carlo
K1 physical conditions
K1 radioastronomy
SP 1
OP 5
LK http://dx.doi.org/https://doi.org/10.1109/WHISPERS.2019.8920859
DO https://doi.org/10.1109/WHISPERS.2019.8920859
SF ELIB - SuUB Bremen

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