Fast sampling of Gaussian Markov random fields Export

@article{citeulike:399817, abstract = { 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.}, author = {Rue}, citeulike-article-id = {399817}, citeulike-linkout-0 = {http://www.ingentaconnect.com/content/bpl/rssb/2001/00000063/00000002/art00288}, issn = {1369-7412}, journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, keywords = {grf, markov}, number = {2}, pages = {325--338}, posted-at = {2009-01-18 19:54:12}, priority = {0}, title = {Fast sampling of Gaussian Markov random fields}, url = {http://www.ingentaconnect.com/content/bpl/rssb/2001/00000063/00000002/art00288}, volume = {63}, year = {2001} }

TY - JOUR ID - citeulike:399817 L3 - citeulike-article-id:399817 N2 - 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. IS - 2 JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology) SN - 1369-7412 EP - 338 TI - Fast sampling of Gaussian Markov random fields VL - 63 SP - 325 KW - grf KW - markov AU - Rue PY - 2001/// UR - http://www.ingentaconnect.com/content/bpl/rssb/2001/00000063/00000002/art00288 ER -