Deep Gaussian Process Emulation using Stochastic Imputation-Reference-Cited by-同舟云学术

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Deep Gaussian Process Emulation using Stochastic Imputation

Published:2022-10-12 Issue:2 Volume:65 Page:150-161
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Ming Deyu¹^ORCID,Williamson Daniel²,Guillas Serge³

Affiliation:

1. School of Management, University College London, London, UK

2. Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

3. Department of Statistical Science, University College London, London, UK

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2022.2124311

Reference39 articles.

1. Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model

2. AAD and least-square Monte Carlo: Fast Bermudan-style options and XVA Greeks

3. Stochastic versions of the em algorithm: an experimental study in the mixture case

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1. Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments;Technometrics;2024-07-08

2. Deep Gaussian process for enhanced Bayesian optimization and its application in additive manufacturing;IISE Transactions;2024-03-11

3. SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes;Advances in Engineering Software;2024-02

4. A mixed-categorical correlation kernel for Gaussian process;Neurocomputing;2023-09

5. Identification and Control of Rehabilitation Robots with Unknown Dynamics: A New Probabilistic Algorithm Based on a Finite-Time Estimator;Mathematics;2023-08-28

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