0000000001216234

AUTHOR

Jakob Runge

showing 2 related works from this author

A perspective on Gaussian processes for Earth observation

2019

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error pr…

FOS: Computer and information sciencesComputer Science - Machine LearningEarth observationComputer scienceDatenmanagement und AnalyseMachine Learning (stat.ML)02 engineering and technology010402 general chemistrycomputer.software_genreStatistics - Applications01 natural sciencesMachine Learning (cs.LG)symbols.namesakeStatistics - Machine LearningApplications (stat.AP)Uncertainty quantificationGaussian processPhysical lawPropagation of uncertaintyMultidisciplinarybusiness.industryPerspective (graphical)gaussian processes021001 nanoscience & nanotechnology0104 chemical sciences13. Climate actionCausal inferenceComputer ScienceGlobal Positioning SystemsymbolsData mining0210 nano-technologybusinesscomputerPerspectivesNational Science Review
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Inferring causation from time series in earth system sciences

2019

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

0301 basic medicineEarth scienceAquatic Ecology and Water Quality ManagementDynamical systems theoryComputer science530 PhysicsDatenmanagement und AnalyseSciencereviewGeneral Physics and Astronomyheart02 engineering and technologyGeneral Biochemistry Genetics and Molecular Biology03 medical and health sciencesDatabasesLife ScienceCausationStatistical physics thermodynamics and nonlinear dynamicsintermethod comparisonlcsh:Scienceresearch workScientific enterpriseMultidisciplinaryWIMEKSeries (mathematics)QComputational sciencefeasibility study500General ChemistryAquatische Ecologie en Waterkwaliteitsbeheersimulation021001 nanoscience & nanotechnologyData sciencecausal inference climateEarth system scienceEnvironmental sciences030104 developmental biologytime series analysisCausal inferencePerspectiveBenchmark (computing)Observational studylcsh:Qconceptual frameworkdata management0210 nano-technologyClimate sciences
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