Quantifying the uncertainty of partitions for infinite mixture models

DOI10.1016/j.spl.2023.109930OpenAlexW4386476351MaRDI QIDQ6067021

Aurore Lavigne, Silvia Liverani

Publication date: 14 December 2023

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2023.109930

zbMATH Keywords

uncertaintyclusteringDirichlet process mixture modelBayesian methods

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)

Cites Work

• Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations
• Methods for merging Gaussian mixture components
• Bayesian cluster analysis: point estimation and credible balls (with discussion)
• Clustering method for censored and collinear survival data
• Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in the Galaxies
• Bayesian Density Estimation and Inference Using Mixtures
• Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression
• Improved criteria for clustering based on the posterior similarity matrix
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