0000000000512835

AUTHOR

David Delgado-gómez

showing 1 related works from this author

Deep Importance Sampling based on Regression for Model Inversion and Emulation

2021

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posteri…

FOS: Computer and information sciencesComputer Science - Machine LearningImportance samplingComputer scienceMonte Carlo methodPosterior probabilityBayesian inferenceInferenceContext (language use)Machine Learning (stat.ML)02 engineering and technologyEstadísticaStatistics - ComputationMachine Learning (cs.LG)symbols.namesakeSurrogate modelStatistics - Machine LearningArtificial Intelligence0202 electrical engineering electronic engineering information engineeringAdaptive regressionEmulationElectrical and Electronic EngineeringModel inversionGaussian processComputation (stat.CO)EmulationApplied Mathematics020206 networking & telecommunicationsRemote sensingComputational Theory and MathematicsSignal Processingsymbols020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionStatistics Probability and UncertaintyAlgorithmImportance sampling
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