Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence

DOI10.1137/20M1320432zbMath1462.62500OpenAlexW3130280819MaRDI QIDQ5858425

Wolfgang Nowak, Emily Cooke, Michael Sinsbeck

Publication date: 13 April 2021

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

Full work available at URL: https://doi.org/10.1137/20m1320432

zbMATH Keywords

marginal likelihoodBayesian model selectionactive learningadaptive samplingsurrogate modelsGaussian process emulators

Mathematics Subject Classification ID

Gaussian processes (60G15) Optimal statistical designs (62K05) Bayesian inference (62F15) Robustness and adaptive procedures (parametric inference) (62F35) Probabilistic models, generic numerical methods in probability and statistics (65C20) Sequential statistical design (62L05)

Related Items (1)

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

Uses Software

• EGO

Cites Work

• Sequential design of computer experiments for the estimation of a probability of failure
• Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
• Efficient global optimization of expensive black-box functions
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
• Sequential Bayesian Polynomial Chaos Model Selection for Estimation of Sensitivity Indices
• Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
• Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems
• Rare Event Simulation of Small Noise Diffusions
• Bayes Factors
• On Information and Sufficiency
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