The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Fast Sampling of Gaussian Markov Random Fields
Open accessAbstract
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.
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Cites
Iterative Methods for Sparse Linear Systems
Saad, Y. (2003) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718003 · Cited by 13,790
Golub, G. H., & Loan, C. F. V. (1996) · Johns Hopkins University Press eBooks · Cited by 6,027
Bayesian image restoration, with two applications in spatial statistics
Besag, J., York, J., & Molli�, A. (1991) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf00116466 · Cited by 4,205
On Gibbs sampling for state space models
CARTER, C. K., & KOHN, R. (1994) · Biometrika · doi:10.1093/biomet/81.3.541 · Cited by 1,917
DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
Frühwirth‐Schnatter, S. (1994) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1994.tb00184.x · Cited by 808
On conditional and intrinsic autoregressions
BESAG, J., & KOOPERBERG, C. (1995) · Biometrika · doi:10.1093/biomet/82.4.733 · Cited by 732
de Veaux, R. D., & Cressie, N. A. C. (1993) · Technometrics · doi:10.2307/1269525 · Cited by 700
Likelihood analysis of non-Gaussian measurement time series
Shephard, N. (1997) · Biometrika · doi:10.1093/biomet/84.3.653 · Cited by 671
LIU, J. S., WONG, W. H., & KONG, A. (1994) · Biometrika · doi:10.1093/biomet/81.1.27 · Cited by 542
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Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Self-citationLindgren, F., Rue, H., & Lindström, J. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2011.00777.x · Cited by 2,708
Self-citationGeneralized structured additive regression based on Bayesian P-splines
Brezger, A., & Lang, S. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.10.011 · Cited by 371
Kastner, G., & Frühwirth-Schnatter, S. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.01.002 · Cited by 287
A note on the determinants of non-fungible tokens returns
Panagiotidis, T., & Papapanagiotou, G. (2025) · International Journal of Finance & Economics · doi:10.1002/ijfe.3008 · Cited by 7
Metadata
- Authors
- Rue, H.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-07-01
- Volume
- 63
- Issue
- 2
- Pages
- 325-338
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2167943787
- Cited by
- 363
- References
- 25
- FWCI
- 21.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
