ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2017
Identifying Mixtures of Mixtures Using Bayesian Estimation.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- BayesCurveFit: Enhancing Curve Fitting for Early-Stage Compound Screening Using Bayesian Inference.bioRxiv : the preprint server for biology · 2025Article
- Deep Mixture of Linear Mixed Models for Complex Longitudinal Data.Statistics in medicine · 2025Article
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Enriched Pitman-Yor processes.Scandinavian journal of statistics, theory and applications · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
- Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.Bayesian analysis · 2024Article
- Spectral Clustering, Bayesian Spanning Forest, and Forest Process.Journal of the American Statistical Association · 2024Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.The annals of applied statistics · 2022Article
- Review of Bayesian selection methods for categorical predictors using JAGS.Journal of applied statistics · 2022Review
- Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes.Journal of applied statistics · 2022Article
- Summarizing Finite Mixture Model with Overlapping Quantification.Entropy (Basel, Switzerland) · 2021Article
- Estimating densities with non-linear support by using Fisher-Gaussian kernels.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2020Article
- Scalable Bayesian Nonparametric Clustering and Classification.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2020Article
- From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering.Advances in data analysis and classification · 2019Article
- Bayesian Distance Clustering.Journal of machine learning research : JMLRArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online.
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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 OpenQuestion graph.