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All Models Are Useful: Bayesian Ensembling for Robust High ..:

, In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining,

Adiga, Aniruddha ; Wang, Lijing ; Hurt, Benjamin... - p. 2505-2513 , 2021

Link: https://dl.acm.org/doi/10.1145/3447548.3467197

RT 
T1 <cTitle>Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3467197&Exemplar=1&LAN=DE
				
A1 Adiga, Aniruddha
A1 Wang, Lijing
A1 Hurt, Benjamin
A1 Peddireddy, Akhil
A1 Porebski, Przemyslaw
A1 Venkatramanan, Srinivasan
A1 Lewis, Bryan Leroy
A1 Marathe, Madhav
PB ACM
YR 2021
K1 Bayesian model averaging
K1 COVID-19
K1 disease forecasting
K1 ensemble
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Machine learning algorithms
K1 Ensemble methods
K1 Modeling and simulation
K1 Model development and analysis
K1 Modeling methodologies
K1 Applied computing
K1 Life and medical sciences
K1 Health informatics
K1 Bioinformatics
K1 Health care information systems
SP 2505
OP 2513
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3447548.3467197
DO https://dl.acm.org/doi/10.1145/3447548.3467197
SF ELIB - SuUB Bremen

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