Bayesian modeling for large spatial datasets

Sudipto Banerjee; Montserrat Fuentes

doi:10.1002/wics.187

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Bayesian modeling for large spatial datasets

Journal article

Peer reviewed

Bayesian modeling for large spatial datasets

Sudipto Banerjee and Montserrat Fuentes

Wiley Interdisciplinary Reviews, Vol.4(1), pp.59-66

01/01/2012

DOI: 10.1002/wics.187

PMCID: PMC3752920

PMID: 23991246

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Abstract

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

Mathematical Models

Data Analysis

Data Processing

Statistics

Computer Applications

Parameter estimation

Costs

Methodology

Spatial discrimination

Markov chains

Statistical methods

Markov analysis

Graphical methods

Modelling

Framework

Monte Carlo method

Statistical analysis

Computer simulation

Probability theory

Data

Spatial data

Computation

Data sets

Models

Bayesian analysis

Monte Carlo simulation

Details

Title: Subtitle: Bayesian modeling for large spatial datasets
Creators: Sudipto Banerjee
Montserrat Fuentes - North Carolina State University
Resource Type: Journal article
Publication Details: Wiley Interdisciplinary Reviews, Vol.4(1), pp.59-66
Publisher: Wiley Subscription Services, Inc; Hoboken
DOI: 10.1002/wics.187
PMID: 23991246
PMCID: PMC3752920
ISSN: 1939-5108
eISSN: 1939-0068
Language: English
Date published: 01/01/2012
Academic Unit: Statistics and Actuarial Science; Biostatistics; Provost Office Administration
Record Identifier: 9983763486802771

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