Spatial point processes and neural networks: A convenient couple
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONThis resource is restricted
https://doi.org/10.1016/j.spasta.2022.100644 |
Metadata
Title
Spatial point processes and neural networks: A convenient coupleDate
2022Publisher
ElsevierISSN
2211-6753Bibliographic citation
MATEU, Jorge; JALILIAN, Abdollah. Spatial point processes and neural networks: A convenient couple. Spatial Statistics, 2022, p. 100644Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S221167532200029XVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
Different spatial point process models and techniques have been developed in the past decades to facilitate the statistical analysis of spatial point patterns. However, in some cases the spatial point process methodology ... [+]
Different spatial point process models and techniques have been developed in the past decades to facilitate the statistical analysis of spatial point patterns. However, in some cases the spatial point process methodology is scarce and no flexible models nor suitable statistical methods are available. For example, due to its complexity, the statistical analysis of spatial point patterns of several groups observed at a number of time instances has not been studied in-depth, and there are a few limited models and methods available for such data. In the present work, we provide a mathematical framework for coupling neural network methods with the statistical analysis of point patterns. In particular, we discuss an example of deep neural networks in the statistical analysis of highly multivariate spatial point patterns and provide a new strategy for building spatio-temporal point processes using variational autoencoder generative neural networks. [-]
Is part of
Spatial Statistics, 2022, p. 100644Funder Name
National Science Foundation | Center for Tropical Forest Science | Smithsonian Tropical Research Institute | John D. and Catherine T. MacArthur Foundation | Small World Institute Fund | Andrew W. Mellon Foundation
Project code
DEB-00753102 | DEB-0129874 | DEB-0346488 | DEB-0425651 | DEB-0640386 | DEB-7922197 | DEB-8206992 | DEB-8605042 | DEB-8906869 | DEB-9100058 | DEB-9221033 | DEB-9405933 | DEB-9615226 | DEB-9909347 | 021115 | 0212284 | 0212818 | NSFDEB021104 | OISE0314581
Rights
© 2022 Elsevier B.V. All rights reserved.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
This item appears in the folowing collection(s)
- INIT_Articles [752]
- MAT_Articles [765]