Fast sampling of Gaussian Markov random fields

Rue Håvard

doi:10.1111/1467-9868.00288

Fast sampling of Gaussian Markov random fields

Rue Håvard^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

263 Scopus citations

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.

Original language	English (US)
Pages (from-to)	325-338
Number of pages	14
Journal	Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume	63
Issue number	2
DOIs	https://doi.org/10.1111/1467-9868.00288
State	Published - 2001
Externally published	Yes

Keywords

Block sampling
Conditional autoregressive model
Divide-and-conquer strategy
Gaussian Markov random field
Markov chain Monte Carlo methods

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1111/1467-9868.00288

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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.",

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year = "2001",

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AB - 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.

KW - Block sampling

KW - Conditional autoregressive model

KW - Divide-and-conquer strategy

KW - Gaussian Markov random field

KW - Markov chain Monte Carlo methods

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JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology

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Fast sampling of Gaussian Markov random fields

Abstract

Keywords

ASJC Scopus subject areas

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