Search Algorithms and Loss Functions for Bayesian Clustering
Article Properties
-
LanguageEnglish
-
DOI (url)
-
Publication Date2022/05/26
-
Indian UGC (journal)
-
Refrences15
-
Citations14
-
David B. Dahl Department of Statistics, Brigham Young University, Provo, UT
-
Devin J. Johnson Department of Statistics, Brigham Young University, Provo, UT ORCID (unauthenticated)
-
Peter Müller Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX
Cite
Dahl, David B., et al. “Search Algorithms and Loss Functions for Bayesian Clustering”. Journal of Computational and Graphical Statistics, vol. 31, no. 4, 2022, pp. 1189-01, https://doi.org/10.1080/10618600.2022.2069779.
Dahl, D. B., Johnson, D. J., & Müller, P. (2022). Search Algorithms and Loss Functions for Bayesian Clustering. Journal of Computational and Graphical Statistics, 31(4), 1189-1201. https://doi.org/10.1080/10618600.2022.2069779
Dahl, David B., Devin J. Johnson, and Peter Müller. “Search Algorithms and Loss Functions for Bayesian Clustering”. Journal of Computational and Graphical Statistics 31, no. 4 (2022): 1189-1201. https://doi.org/10.1080/10618600.2022.2069779.
Dahl DB, Johnson DJ, Müller P. Search Algorithms and Loss Functions for Bayesian Clustering. Journal of Computational and Graphical Statistics. 2022;31(4):1189-201.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
You May Also Like
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 2010 | |||
| 10.1214/17-BA1073 | ||||
| Optimal Bayesian estimators for latent variable cluster models | Statistics and Computing |
| 27 | 2017 |
| Objective Criteria for the Evaluation of Clustering Methods | Journal of the American Statistical Association |
| 2,337 | 1971 |
| Dependent Modeling of Temporal Sequences of Random Partitions | Journal of Computational and Graphical Statistics |
| 7 | 2021 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Convergence Diagnostics for Entity Resolution | Annual Review of Statistics and Its Application |
| 2024 | |
| Joint Random Partition Models for Multivariate Change Point Analysis | Bayesian Analysis | 2024 | ||
| Joint Random Partition Models for Multivariate Change Point Analysis | Bayesian Analysis | 2024 | ||
Entropy regularization in probabilistic clustering | Statistical Methods & Applications |
| 2023 | |
| A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States | Computational Statistics & Data Analysis |
| 2023 |
Citations Analysis
The category
Science: Mathematics: Probabilities. Mathematical statistics 9
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment and was published in 2022. The most recent citation comes from a 2024 study titled Convergence Diagnostics for Entity Resolution. This article reached its peak citation in 2023, with 7 citations. It has been cited in 11 different journals, 9% of which are open access. Among related journals, the Bayesian Analysis cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.