Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package

DOI10.1016/j.csda.2013.03.018zbMath1471.62161OpenAlexW2023668266MaRDI QIDQ1621383

David Ginsbourger, Victor Picheny

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://boris.unibe.ch/41533/8/Noisy%20kriging-based%20optimization%20methods%20a%20unified%20implementation%20within%20the%20DiceOptim%20package.pdf

zbMATH Keywords

Gaussian processescomputer experimentsactive learning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Software, source code, etc. for problems pertaining to statistics (62-04)

Related Items (6)

Special issue on statistical algorithms and software in R ⋮ Conditional optimization of a noisy function using a kriging metamodel ⋮ TREGO: a trust-region framework for efficient global optimization ⋮ Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise ⋮ DiceOptim ⋮ Analyzing stochastic computer models: a review with opportunities

Uses Software

• R
• geoR
• EGO
• DiceKriging
• tgp
• lhs
• mlegp
• nloptr
• DiceDesign
• pso
• dfoptim
• MORET

Cites Work

• Unnamed Item
• Nonlinear methods for inverse statistical problems
• Sequential design of computer experiments for the estimation of a probability of failure
• An efficient methodology for modeling complex computer codes with Gaussian processes
• On applying Kriging-based approximate optimization to inaccurate data
• Efficient global optimization of expensive black-box functions
• A taxonomy of global optimization methods based on response surfaces
• Global optimization of stochastic black-box systems via sequential kriging meta-models
• Bayesian Calibration of Computer Models
• The Correlated Knowledge Gradient for Simulation Optimization of Continuous Parameters using Gaussian Process Regression
• Estimating Percentiles of Uncertain Computer Code Outputs
• Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

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