Fast Sampling of Gaussian Markov Random Fields
- 1 July 2001
- journal article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 63 (2) , 325-338
- https://doi.org/10.1111/1467-9868.00288
Abstract
Summary: 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.This publication has 0 references indexed in Scilit: