Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data

DOI10.1080/10618600.2018.1482761OpenAlexW2811431274MaRDI QIDQ3391187

Mark R. Bass, Sujit K. Sahu

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2018.1482761

zbMATH Keywords

rate of convergenceparameterizationGibbs samplingspatially varying coefficient model

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Multilevel linear models, Gibbs samplers and multigrid decompositions (with discussion) ⋮ A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC

Uses Software

• CODA
• ggplot2
• maptools
• spBayes
• FRK
• spTimer

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Spatial variation. 2nd ed
• A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC
• Inference from iterative simulation using multiple sequences
• A spatio-temporal downscaler for output from numerical models
• A Spatiotemporal Model for Mexico City Ozone Levels
• Sampling-Based Approaches to Calculating Marginal Densities
• Fixed Rank Kriging for Very Large Spatial Data Sets
• High-Resolution Space–Time Ozone Modeling for Assessing Trends
• Spatial Modeling With Spatially Varying Coefficient Processes
• Efficient parametrisations for normal linear mixed models
• A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes With Uncertain Forest Areas
• Probabilistic Forecasts, Calibration and Sharpness
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics

This page was built for publication: Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data