Design of monitoring networks using \(k\)-determinantal point processes

DOI10.1002/ENV.2483zbMATH Open1545.62723MaRDI QIDQ6625891

James V. Zidek, Nhu D. Le, Y. Wang, C. M. Casquilho-Resende

Publication date: 28 October 2024

Published in: Environmetrics (Search for Journal in Brave)

zbMATH Keywords

surveillanceentropyGaussian fieldsdeterminantal point processesspatial design

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Optimal design in geostatistics under preferential sampling
• Reducing estimation bias in adaptively changing monitoring networks with preferential site selection
• Design of computer experiments: space filling and beyond
• Determinantal processes and independence
• Distributions on partitions, point processes, and the hypergeometric kernel
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Eynard-Mehta theorem, Schur process, and their Pfaffian analogs
• Determinantal point processes for machine learning
• Bayesian geostatistical modelling with informative sampling locations
• Fixed Rank Kriging for Very Large Spatial Data Sets
• Gaussian Predictive Process Models for Large Spatial Data Sets
• The coincidence approach to stochastic point processes
• An Exact Algorithm for Maximum Entropy Sampling
• Designing and Integrating Composite Networks for Monitoring Multivariate Gaussian Pollution Fields
• Determinantal Point Process Models and Statistical Inference

This page was built for publication: Design of monitoring networks using \(k\)-determinantal point processes

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6625891)