Sampling from gaussian markov random fields conditioned on linear constraints

Daniel Peter Simpson, Ian W. Turner, A. N. Pettitt

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2 Citations (Scopus)

Abstract

Gaussian Markov random fields (gmrfs) are important modeling tools in statistics. They are often utilised to model spatially struc-tured uncertainty, seasonal variation and other trends in the data. These last two examples of gmrfs are part of a larger class of gmrfs conditioned on linear constraints. Performing Monte Carlo Markov Chain inference on these models requires a large number of samples from gmrfs conditioned on linear constraints. Therefore it is vital to have fast and eficient methods for performing these samples. This article presents three Krylov subspace methods for sampling from a gmrf conditioned on linear constraints based on solving a Karush-Kuhn{Tucker, or saddle point, system.

Original languageEnglish
Pages (from-to)C1041-C1053
JournalANZIAM Journal
Volume48
DOIs
Publication statusPublished - 2008
Externally publishedYes

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