The DOI matches its Crossref record.
Gaussian Markov Random Fields
Abstract
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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-citationFace detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248014 · Cited by 2,120
A century of trends in adult human height
NCD Risk Factor Collaboration (NCD-RisC) (2016) · eLife · doi:10.7554/elife.13410 · Cited by 1,558
The WHO estimates of excess mortality associated with the COVID-19 pandemic
Msemburi, W., Karlinsky, A., Knutson, V., Aleshin-Guendel, S., Chatterji, S., & Wakefield, J. (2023) · Nature · doi:10.1038/s41586-022-05522-2 · Cited by 1,064
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017) · Statistical Science · doi:10.1214/16-sts576 · Cited by 1,085
Self-citationBayesian Computing with INLA: A Review
Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., & Lindgren, F. K. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054045 · Cited by 737
Self-citationGaussian Conditional Random Field Network for Semantic Segmentation
Vemulapalli, R., Tuzel, O., Liu, M.-Y., & Chellappa, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.351 · Cited by 150
ABC likelihood-free methods for model choice in Gibbs random fields
Grelaud, A., Marin, J.-M., Robert, C. P., Rodolphe, F., & Taly, J.-F. (2009) · Bayesian Analysis · doi:10.1214/09-ba412 · Cited by 119
(2023) · doi:10.1214/22-aoas1673 · Cited by 72
Diminishing benefits of urban living for children and adolescents' growth and development
NCD Risk Factor Collaboration (NCD-RisC), Mishra, A., Zhou, B., Rodriguez-Martinez, A., Bixby, H., Singleton, R. K., Carrillo-Larco, R. M., Sheffer, K. E., Paciorek, C. J., Bennett, J. E., et al. (2023) · Nature · doi:10.1038/s41586-023-05772-8 · Cited by 70
Robustness and bias of European excess death estimates in 2020 under varying model specifications
Schöley, J. (2021) · doi:10.1101/2021.06.04.21258353 · Cited by 42
An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics
Lan, S., Palacios, J. A., Karcher, M., Minin, V. N., & Shahbaba, B. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv378 · Cited by 38
Damien, B. G., Sode, A. I., Bocossa, D., Elanga-Ndille, E., Aguemon, B., Corbel, V., Henry, M.-C., Glèlè Kakaï, R. L., & Remoué, F. (2022) · BMC Public Health · doi:10.1186/s12889-022-14032-9 · Cited by 18
Scale Space Methods for Analysis of Type 2 Diabetes Patients′ Blood Glucose Values
Skrøvseth, S. O., & Godtliebsen, F. (2011) · Computational and Mathematical Methods in Medicine · doi:10.1155/2011/672039 · Cited by 11
Metadata
- Authors
- Rue, H., & Held, L.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2005-02-18
- OpenAlex
- W143236119
- Cited by
- 1,823
- References
- 0
- FWCI
- 26.97
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.