Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC

DOI10.1007/978-3-030-43465-6_18OpenAlexW3044330023MaRDI QIDQ5117940

Déborah Idier, François Bachoc, Andrés F. López-Lopera, Nicolas Durrande, Olivier Roustant, Jeremy Rohmer

Publication date: 26 August 2020

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.04827

zbMATH Keywords

linear inequality constraintsMonte Carlo methodsGaussian processes emulatorsMonte Carlo and Markov chainuncertainty quantification with noisy observations

Mathematics Subject Classification ID

Monte Carlo methods (65C05)

Related Items (1)

Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints

Uses Software

• DiceKriging
• DiceOptim
• kergp
• DiceDesign
• PMTK
• DiceEval
• lineqGPR
• profExtrema
• tmg

Cites Work

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• Generalization of the Kimeldorf-Wahba correspondence for constrained interpolation
• Kriging of financial term-structures
• Gaussian process emulators for computer experiments with inequality constraints
• The Finite Element Method: Theory, Implementation, and Applications
• Monotone Emulation of Computer Experiments
• Sampling Constrained Probability Distributions Using Spherical Augmentation
• A New Rejection Sampling Method for Truncated Multivariate Gaussian Random Variables Restricted to Convex Sets
• Practical Markov Chain Monte Carlo
• Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints
• The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting

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