Statistical deep learning for spatial and spatiotemporal data
CK Wikle, A Zammit-Mangion - Annual Review of Statistics and …, 2023 - annualreviews.org
Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Statistical deep learning for spatial and spatio-temporal data
CK Wikle, A Zammit-Mangion - arXiv preprint arXiv:2206.02218, 2022 - arxiv.org
Deep neural network models have become ubiquitous in recent years, and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
To achieve scalable and accurate inference for latent Gaussian processes, we propose a
variational approximation based on a family of Gaussian distributions whose covariance …
variational approximation based on a family of Gaussian distributions whose covariance …
Non-stationary Gaussian process surrogates
We provide a survey of non-stationary surrogate models which utilize Gaussian processes
(GPs) or variations thereof, including non-stationary kernel adaptations, partition and local …
(GPs) or variations thereof, including non-stationary kernel adaptations, partition and local …
Traditional kriging versus modern Gaussian processes for large‐scale mining data
RB Christianson, RM Pollyea… - Statistical Analysis and …, 2023 - Wiley Online Library
The canonical technique for nonlinear modeling of spatial/point‐referenced data is known
as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling …
as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling …
Free-form variational inference for Gaussian process state-space models
Gaussian process state-space models (GPSSMs) provide a principled and flexible approach
to modeling the dynamics of a latent state, which is observed at discrete-time points via a …
to modeling the dynamics of a latent state, which is observed at discrete-time points via a …
Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines
E Ahishakiye, F Kanobe - Discover Artificial Intelligence, 2024 - Springer
Background Cervical cancer is the fourth most frequent cancer in women worldwide. Even
though cervical cancer deaths have decreased significantly in Western countries, low and …
though cervical cancer deaths have decreased significantly in Western countries, low and …
[HTML][HTML] SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare
There have been impressive advances in the physical and mathematical modelling of
complex physiological systems in the last few decades, with the potential to revolutionise …
complex physiological systems in the last few decades, with the potential to revolutionise …
Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes
Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate models
of complex computer experiments when response surface dynamics are non-stationary …
of complex computer experiments when response surface dynamics are non-stationary …
Robust expected improvement for Bayesian optimization
RB Christianson, RB Gramacy - IISE Transactions, 2024 - Taylor & Francis
Abstract Bayesian Optimization (BO) links Gaussian Process (GP) surrogates with
sequential design toward optimizing expensive-to-evaluate black-box functions. Example …
sequential design toward optimizing expensive-to-evaluate black-box functions. Example …