EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors

DOI10.1137/19M1288462zbMath1465.60033arXiv2203.10130OpenAlexW3151533427MaRDI QIDQ5858428

C. Devon Lin, Abhyuday Mandal, Qian Xiao, Xinwei Deng

Publication date: 13 April 2021

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

Full work available at URL: https://arxiv.org/abs/2203.10130

zbMATH Keywords

emulatorkrigingbig dataadditive modelcategorical input

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15)

Related Items (1)

Modeling and Active Learning for Experiments with Quantitative-Sequence Factors

Uses Software

• rgenoud
• GPfit

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology
• Kriging metamodeling in simulation: a review
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Optimal maximin \(L_{1}\)-distance Latin hypercube designs based on good lattice point designs
• Penalized blind kriging in computer experiments
• Design for computer experiments with qualitative and quantitative factors
• ANALYSIS OF PROTEIN ACTIVITY DATA BY GAUSSIAN STOCHASTIC PROCESS MODELS
• Construction of Maximin Distance Designs via Level Permutation and Expansion
• Construction of maximin distance Latin squares and related Latin hypercube designs

This page was built for publication: EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors