Locally induced Gaussian processes for large-scale simulation experiments
From MaRDI portal
Publication:2058747
DOI10.1007/s11222-021-10007-9zbMath1475.62023arXiv2008.12857OpenAlexW3154293753MaRDI QIDQ2058747
Ryan B. Christianson, D. Austin Cole, Robert B. Gramacy
Publication date: 9 December 2021
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2008.12857
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Cites Work
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- Hilbert space methods for reduced-rank Gaussian process regression
- Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior
- Exploratory designs for computational experiments
- Variational inference for sparse spectrum Gaussian process regression
- Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology
- Parameter estimation in high dimensional Gaussian distributions
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Computer experiment designs for accurate prediction
- The design and analysis of computer experiments
- Recursive estimation for sparse Gaussian process regression
- A supermartingale approach to Gaussian process based sequential design of experiments
- Sparse On-Line Gaussian Processes
- Massively Parallel Approximate Gaussian Process Regression
- Spectral Approximation of the IMSE Criterion for Optimal Designs in Kernel-Based Interpolation Models
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
- Stochastic Kriging for Simulation Metamodeling
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature
- Exploiting Variance Reduction Potential in Local Gaussian Process Search
- Approximating Likelihoods for Large Spatial Data Sets
- A Limited Memory Algorithm for Bound Constrained Optimization
- Knot selection in sparse Gaussian processes with a variational objective function
- Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions
- Emulating Satellite Drag from Large Simulation Experiments
- Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation
- Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes
- A general framework for Vecchia approximations of Gaussian processes