Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processes

Kentaro Matsuura

doi:10.1007/978-981-19-4755-1_12

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Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processes

Kentaro Matsuura

Year: 2022 Pages: 285-329

DOI: 10.1007/978-981-19-4755-1_12

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Keywords:

Generalization Gaussian Gaussian process Random field Computer science Gaussian random field Markov chain Spatial analysis Geospatial analysis Range (aeronautics) Grid Markov process Extension (predicate logic) Algorithm Data mining Pattern recognition (psychology) Statistical physics Mathematics Artificial intelligence Geography Machine learning Statistics Cartography Physics Engineering

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Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

Data Management and Algorithms

Physical Sciences → Computer Science → Signal Processing

Bayesian Modeling and Causal Inference

Physical Sciences → Computer Science → Artificial Intelligence

Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processes

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