ReviewPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2023
Bayesian cluster analysis.
Review in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.Bioinformatics (Oxford, England) · 2026Article
- A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Article
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Assessing the impact of a wheat flour and baker's yeast restricted diet vs. calorie restriction in non-alcoholic fatty liver disease patients.Archives of medical science : AMS · 2025Article
- The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics.Heliyon · 2024Article
- Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation.Frontiers in immunology · 2024Article
- A special issue on Bayesian inference: challenges, perspectives and prospects.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.