The DOI matches its Crossref record.
Efficiency of pseudolikelihood estimation for simple Gaussian fields
Abstract
The efficiency of parameter estimation using a pseudolikelihood technique is assessed for some simple spatial Gaussian schemes.
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
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862
(2005) · doi:10.5555/1046920.1088696 · Cited by 756
Herding dynamical weights to learn
Welling, M. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553517 · Cited by 401
Logit models and logistic regressions for social networks: II. Multivariate relations
Pattison, P., & Wasserman, S. (1999) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711099159053 · Cited by 327
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316
Metadata
- Authors
- BESAG, J.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1977
- Volume
- 64
- Issue
- 3
- Pages
- 616-618
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2053476892
- Cited by
- 321
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficiency of pseudolikelihood estimation for simple Gaussian fields. https://app.cochat.ai/papers/10.1093/biomet/64.3.616
- The paper
- BESAG, J. (1977). Efficiency of pseudolikelihood estimation for simple Gaussian fields. Biometrika. https://doi.org/10.1093/biomet/64.3.616
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.
