Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis

Keyphrases

• Particulate Matter 100%
• Non-separable 100%
• Nearest Neighbor Gaussian Process 100%
• Spatio-temporal Data 100%
• Gaussian Process Model 100%
• Spatiotemporal Gaussian Process 33%
• Human Health 16%
• High-resolution 16%
• Storage Requirement 16%
• Curb 16%
• Europe 16%
• Covariance Structure 16%
• LOTOS-EUROS 16%
• Concentration Limit 16%
• Bayesian Hierarchical Model 16%
• Chemistry Transport Model 16%
• Process Approach 16%
• Spatio-temporal Random Effects 16%
• Sparsity-inducing Prior 16%
• Predictive Uncertainty 16%
• Memory Requirements 16%
• Novel Class 16%
• Computational Challenges 16%
• Air Quality Dataset 16%
• Low-rank Approximation 16%
• Posterior Inference 16%
• Sparse Approximation 16%
• Gaussian Process 16%
• Red Flags 16%
• Environmental Pollutants 16%
• Regulatory Efforts 16%

Computer Science

• gaussian process model 100%
• Spatiotemporal Data 100%
• Storage Requirement 25%
• Sparsity 25%
• Approximation (Algorithm) 25%
• Large Data Set 25%
• Rank Approximation 25%
• Memory Requirement 25%
• Hierarchical Bayesian Model 25%
• Sparse Approximation 25%
• Transport Model 25%

Mathematics

• Gaussian Process 100%
• Nearest Neighbor 100%
• Covariance Structure 11%
• Numerical Analysis 11%
• Bayesian 11%
• Hierarchical Model 11%
• Random Effect 11%
• Low-Rank Approximation 11%