Recent advances in Bayesian optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …
spatial statistical modelling and geostatistics. The specification through the covariance …
[ΒΙΒΛΙΟ][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 …
[ΒΙΒΛΙΟ][B] Animal movement: statistical models for telemetry data
The study of animal movement has always been a key element in ecological science,
because it is inherently linked to critical processes that scale from individuals to populations …
because it is inherently linked to critical processes that scale from individuals to populations …
Local Gaussian process approximation for large computer experiments
We provide a new approach to approximate emulation of large computer experiments. By
focusing expressly on desirable properties of the predictive equations, we derive a family of …
focusing expressly on desirable properties of the predictive equations, we derive a family of …
Gaussian predictive process models for large spatial data sets
With scientific data available at geocoded locations, investigators are increasingly turning to
spatial process models for carrying out statistical inference. Over the last decade …
spatial process models for carrying out statistical inference. Over the last decade …
Cross-covariance functions for multivariate geostatistics
Continuously indexed datasets with multiple variables have become ubiquitous in the
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …
Strictly and non-strictly positive definite functions on spheres
T Gneiting - 2013 - projecteuclid.org
Supplement to “Strictly and non-strictly positive definite functions on spheres”. Appendix A
states and proves further criteria of Pólya type, thereby complementing Section 4.2 …
states and proves further criteria of Pólya type, thereby complementing Section 4.2 …
[ΒΙΒΛΙΟ][B] Local models for spatial analysis
CD Lloyd - 2010 - books.google.com
With new chapters addressing spatial patterning in single variables and spatial relations,
this second edition provides guidance to a wide variety of real-world problems. Focusing on …
this second edition provides guidance to a wide variety of real-world problems. Focusing on …
[ΒΙΒΛΙΟ][B] Random fields for spatial data modeling
DT Hristopulos - 2020 - Springer
The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …
and robust GIScience methods for use in transdisciplinary problem solving and decision …