Conjugate Bayesian Regression Models for Massive Geostatistical Data Sets

DOI10.1007/978-3-030-42196-0_10OpenAlexW3048746511MaRDI QIDQ5050414

Sudipto Banerjee

Publication date: 15 November 2022

Published in: Emerging Topics in Statistics and Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-42196-0_10

zbMATH Keywords

spatial processesBayesspatial statisticsvariogramposterior distributionlinear regressionGISmassive spatial dataspatiotemporal models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• spBayes
• BayesDA

Cites Work

• Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
• High-dimensional Bayesian geostatistics
• A case study competition among methods for analyzing large spatial data
• Handbook of Spatial Statistics
• Gaussian Predictive Process Models for Large Spatial Data Sets
• Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments^†
• Unnamed Item

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