Design of monitoring networks using \(k\)-determinantal point processes
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Publication:6625891
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)
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
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