Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems

DOI10.1137/15M1047659zbMath1387.62093OpenAlexW2736380262MaRDI QIDQ4636401

Wolfgang Nowak, Michael Sinsbeck

Publication date: 19 April 2018

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/15m1047659

zbMATH Keywords

Kullback-Leibler divergenceBayesian inferenceadaptive samplingsurrogate modelsGaussian process emulatorscode uncertainty

Mathematics Subject Classification ID

Related Items (7)

Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework ⋮ Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark ⋮ Posterior consistency for Gaussian process approximations of Bayesian posterior distributions ⋮ Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems ⋮ Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations ⋮ A Sequential Design Approach for Calibrating Dynamic Computer Simulators ⋮ Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence

Uses Software

• EGO
• BayesDA

Cites Work

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