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Fast sampling of Gaussian Markov random fields
Author(s) -
Rue Håvard
Publication year - 2001
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00288
Subject(s) - markov chain , markov chain monte carlo , variable order markov model , computer science , gaussian , random field , algorithm , normalization (sociology) , markov property , markov model , mathematics , statistical physics , artificial intelligence , machine learning , statistics , bayesian probability , physics , quantum mechanics , sociology , anthropology
This paper demonstrates how Gaussian Markov random fields (conditional autoregressions) can be sampled quickly by using numerical techniques for sparse matrices. The algorithm is general and efficient, and expands easily to various forms for conditional simulation and evaluation of normalization constants. We demonstrate its use by constructing efficient block updates in Markov chain Monte Carlo algorithms for disease mapping.