Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments-Reference-Cited by-同舟云学术

Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments

Published:2018-07-11 Issue:4 Volume:27 Page:808-821
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Binois Mickaël¹^ORCID,Gramacy Robert B.²,Ludkovski Mike³

Affiliation:

1. Booth School of Business, University of Chicago, Chicago, IL

2. Department of Statistics, Virginia Tech, Hutcheson Hall, Blacksburg, VA

3. Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA

Funder

Division of Mathematical Sciences

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2018.1458625

Reference41 articles.

1. Stochastic Kriging for Simulation Metamodeling

2. Composite Gaussian process models for emulating expensive functions

3. Gaussian predictive process models for large spatial data sets

4. Boukouvalas, A., and Cornford, D. (2009), “Learning Heteroscedastic Gaussian Processes for Complex Datasets,” Tech. Rep., Aston University, Neural Computing Research Group.

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