A Bayesian sparse finite mixture model for clustering data from a heterogeneous population

DOI10.1214/18-BJPS425zbMath1445.62152OpenAlexW3021168344MaRDI QIDQ783308

Erlandson F. Saraiva, Adriano K. Suzuki, Luis Aparecido Milan

Publication date: 12 August 2020

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bjps/1588579224

zbMATH Keywords

Metropolis-Hastings algorithmGibbs samplingmixture modelsplit-merge updateBayesian approach

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Applications of statistics to physics (62P35) Galactic and stellar dynamics (85A05) Statistical astronomy (85A35)

Uses Software

• AS 136

Cites Work

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• Model-based clustering based on sparse finite Gaussian mixtures
• Algorithm AS 136: A K-Means Clustering Algorithm
• Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Estimating the dimension of a model
• Model-based clustering of high-dimensional data: a review
• From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering
• Mixture models, latent variables and partitioned importance sampling
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Bayesian cluster analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Dealing With Label Switching in Mixture Models
• Finite mixture models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Bayesian Measures of Model Complexity and Fit
• Bayesian Density Estimation and Inference Using Mixtures
• Partitioning gene expression data by data-driven Markov chain Monte Carlo
• A Framework for Feature Selection in Clustering
• A new look at the statistical model identification

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