30 Years of space–time covariance functions
In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
[图书][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
A review of geospatial exposure models and approaches for health data integration
Background Geospatial methods are common in environmental exposure assessments and
increasingly integrated with health data to generate comprehensive models of …
increasingly integrated with health data to generate comprehensive models of …
Efficient algorithms for Bayesian nearest neighbor Gaussian processes
We consider alternate formulations of recently proposed hierarchical nearest neighbor
Gaussian process (NNGP) models for improved convergence, faster computing time, and …
Gaussian process (NNGP) models for improved convergence, faster computing time, and …
Cola: Exploiting compositional structure for automatic and efficient numerical linear algebra
Many areas of machine learning and science involve large linear algebra problems, such as
eigendecompositions, solving linear systems, computing matrix exponentials, and trace …
eigendecompositions, solving linear systems, computing matrix exponentials, and trace …
Sparse Cholesky Factorization by Kullback--Leibler Minimization
We propose to compute a sparse approximate inverse Cholesky factor L of a dense
covariance matrix Θ by minimizing the Kullback--Leibler divergence between the Gaussian …
covariance matrix Θ by minimizing the Kullback--Leibler divergence between the Gaussian …
Large-scale gaussian processes via alternating projection
Training and inference in Gaussian processes (GPs) require solving linear systems with $
n\times n $ kernel matrices. To address the prohibitive $\mathcal {O}(n^ 3) $ time complexity …
n\times n $ kernel matrices. To address the prohibitive $\mathcal {O}(n^ 3) $ time complexity …
The Matérn model: A journey through statistics, numerical analysis and machine learning
The Matern Model: A Journey Through Statistics, Numerical Analysis and Machine Learning
Page 1 Statistical Science 2024, Vol. 39, No. 3, 469–492 https://doi.org/10.1214/24-STS923 © …
Page 1 Statistical Science 2024, Vol. 39, No. 3, 469–492 https://doi.org/10.1214/24-STS923 © …
Vecchia-approximated deep Gaussian processes for computer experiments
Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
Variational nearest neighbor Gaussian process
Variational approximations to Gaussian processes (GPs) typically use a small set of
inducing points to form a low-rank approximation to the covariance matrix. In this work, we …
inducing points to form a low-rank approximation to the covariance matrix. In this work, we …