Deep Gaussian Processes for geophysical parameter retrieval

• URL: http://arxiv.org/abs/2012.12099v1
• Date: Mon, 7 Dec 2020 14:44:04 GMT
• Title: Deep Gaussian Processes for geophysical parameter retrieval
• Authors: Daniel Heestermans Svendsen, Pablo Morales-\'Alvarez, Rafael Molina, Gustau Camps-Valls
• Abstract summary: This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, and improves prediction accuracy over standard full and sparse GP models.
• Score: 15.400481898772158
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides an efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence of performance for estimation of surface dew point temperature from infrared sounding data.

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