Data fusion with Gaussian processes for estimation of environmental hazard events
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Publication:6626381
DOI10.1002/env.2660zbMATH Open1545.62981MaRDI QIDQ6626381
Benjamin D. Youngman, Theodoros Economou, Xiaoyu Xiong
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
Gaussian processesdata integrationmodel validationspatial interpolationnatural hazardschange-of-supportEuropean windstorm
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Model-based geostatistics.
- High-dimensional Bayesian geostatistics
- A statistical framework to combine multivariate spatial data and physical models for hurricane surface wind prediction
- Bayesian Calibration of Computer Models
- Learning about physical parameters: the importance of model discrepancy
- Bayesian Forecasting for Complex Systems Using Computer Simulators
- Inference for Deterministic Simulation Models: The Bayesian Melding Approach
- Adaptive multiple importance sampling for Gaussian processes
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support
- Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near-road environment
- Multivariate air pollution prediction modeling with partial missingness
- Error in estimating area-level air pollution exposures for epidemiology
- Spatio-temporal data fusion for massive sea surface temperature data from MODIS and AMSR-E instruments
- A joint Bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
- Combining spatial information sources while accounting for systematic errors in proxies
- Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution
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