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

Journal of Biometrics & Biostatistics

article

Yu Ryan Yue¹  Xiao-Feng Wang² 

[1] Department of Statistics and CIS, Zicklin School of Business, Baruch College, The City University of New York;Department of Quantitative Health Sciences / Biostatistics Section, Cleveland Clinic Lerner Research Institute

关键词: Gaussian Markov random fields;    Markov chain Monte Carlo;    spatial statistics;    Cholesky factorization;    Integrated nested laplace approximation;   

DOI  :  10.4172/2155-6180.1000e128

来源: Hilaris Publisher

【 摘 要 】

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.

【 授权许可】

Unknown