Spatial Gaussian Markov Random Fields: Modelling, Applications and Efficient Computations

Authors

Yu Ryan Yue, CUNY Bernard M Baruch CollegeFollow
Xiao-Feng Wang, Cleveland Clinic Lerner Research Institute

Document Type

Other

Publication Date

2014

Abstract

A powerful modelling tool for spatial data is the framework of Gaussian Markov random fields (GMRFs), which are discrete domain Gaussian random fields equipped with a Markov property. GMRFs allow us to combine the analytical results for the Gaussian distribution as well as Markov properties, thus allow for the development of computationally efficient algorithms. Here we briefly review popular spatial GMRFs, show how to construct them, and outline their recent developments and possible future work.

Comments

This article was originally published in the Journal of Biometrics and Biostatistics, available at DOI: 10.472/2155-6180.1000e128.

This is an open-access article distributed under the terms of the Creative Commons Attribution License.