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Bayesian modeling for large spatial datasets
Author(s) -
Banerjee Sudipto,
Fuentes Montserrat
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.187
Subject(s) - markov chain monte carlo , computer science , bayesian probability , data mining , spatial analysis , graphical model , variable order bayesian network , bayesian inference , monte carlo method , rank (graph theory) , machine learning , artificial intelligence , statistics , mathematics , combinatorics
We focus upon flexible Bayesian hierarchical models for scientific data available at geo‐coded locations. Investigators are increasingly turning to spatial process models to analyze such datasets. These models are customarily estimated using Markov Chain Monte Carlo (MCMC) methods, which have become especially popular for spatial modeling, given their flexibility and power to fit models that would be infeasible otherwise. However, estimating Bayesian spatial process models is undermined by prohibitive computational expenses associated with parameter estimation. Classes of low‐rank spatial process models are increasingly being deployed to resolve this problem by projecting spatial effects to a lower‐dimensional subspace. We discuss how a low‐rank process called the ‘predictive process’ seamlessly enters the hierarchical modeling framework and helps us accrue substantial computational benefits. WIREs Comp Stat 2012, 4:59–66. doi: 10.1002/wics.187 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Data: Types and Structure > Image and Spatial Data Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Data: Types and Structure > Image and Spatial Data