Bayesian multivariate process modeling for prediction of forest attributes

DOI10.1198/108571108X273160zbMath1306.62272MaRDI QIDQ2259847

Andrew O. Finley, Alan R. Ek, Ronald E. McRoberts, Sudipto Banerjee

Publication date: 5 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

zbMATH Keywords

Monte CarloMarkov chainBayesian inferencecoregionalizationmultivariate spatial processforest inventory

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Geostatistics (86A32)

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Uses Software

• spBayes
• BayesDA

Cites Work

• MCMC algorithms for constrained variance matrices
• Bayesian multi-resolution modeling for spatially replicated data sets with application to forest biomass data
• Interpolation of spatial data. Some theory for kriging
• Nonstationary multivariate process modeling through spatially varying coregionalization
• Multivariate spatial modeling for geostatistical data using convolved covariance functions
• Coregionalized Single- and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth
• Nonconjugate Bayesian Estimation of Covariance Matrices and Its Use in Hierarchical Models
• Bayesian Measures of Model Complexity and Fit
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