TEKNISK - NATURVITENSKAPELIGE UNIVERSITET Marginal Variances for Gaussian Markov Random Fields
نویسنده
چکیده
Gaussian Markov random fields (GMRFs) are specified conditionally by its precision matrix meaning that its inverse, the covariance matrix, is not explicitly known. Computing the often dense covariance matrix directly using matrix inversion is often unfeasible due to time and memory requirement. In this note, we discuss a simple and fast algorithm to compute the marginal variances for a GMRF. We also provide extensions to deal with linear soft and hard constraints, essentially without extra costs.
منابع مشابه
Norges Teknisk-naturvitenskapelige Universitet Fitting Gaussian Markov Random Fields to Gaussian Fields Fitting Gaussian Markov Random Fields to Gaussian Fields Tmr Project on Spatial Statistics (erb-fmrx-ct960095) for Support and Inspiration
SUMMARY This paper discusses the following task often encountered building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in observed data or consistent with prior knowledge. The Markov property is essential in design of computational efficient Markov chain Monte Carlo algorithms used to analyse suc...
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