Fast Sampling of Gaussian Markov Random Fields 论文
2001Journal of the Royal Statistical Society Series B (Statistical Methodology)引用 365
Statistical Methods and InferenceStatistical Methods and Bayesian InferenceGaussian Processes and Bayesian Inference
摘要
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.