scholarly journals Projection predictive model selection for Gaussian processes

2016 ◽  
Author(s):  
Juho Piironen ◽  
Aki Vehtari
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Gaussian Processes ◽  
Selection For

scholarly journals Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks

2020 ◽  
Author(s):  
Parul Awasthy ◽  
Bishwaranjan Bhattacharjee ◽  
John Kender ◽  
Radu Florian
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Transfer Learning ◽  
Sequence Labeling ◽  
Selection For

scholarly journals Optimization of Aerosol Model Selection for TROPOMI/S5P

2021 ◽  
Vol 13 (13) ◽  
pp. 2489
Author(s):  
Lanlan Rao ◽  
Jian Xu ◽  
Dmitry S. Efremenko ◽  
Diego G. Loyola ◽  
Adrian Doicu
Keyword(s):  
Model Selection ◽  
Insufficient Information ◽  
Satellite Measurements ◽  
Model Uncertainties ◽  
Bayesian Algorithm ◽  
Layer Height ◽  
Aerosol Model ◽  
Selection For ◽  
Temporal Inhomogeneity ◽  
Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

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