Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions

DOI10.1007/978-981-15-1960-4_6zbMath1445.62328OpenAlexW2996961133MaRDI QIDQ3305490

Emmanuel Barbier, Benjamin Lemasson, Alexis Arnaud, Florence Forbes

Publication date: 7 August 2020

Published in: Communications in Computer and Information Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-981-15-1960-4_6

zbMATH Keywords

Bayesian analysisEM algorithmBayesian model selectionvariational approximationGaussian scale mixture

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of big data and data science (62R07)

Related Items (2)

Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets ⋮ A survey on model-based co-clustering: high dimension and estimation challenges

Uses Software

• mclust
• CAPUSHE

Cites Work

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• Slope heuristics: overview and implementation
• Robust Bayesian clustering
• Variational approximations in Bayesian model selection for finite mixture distributions
• Finite mixture and Markov switching models.
• Model-Based Gaussian and Non-Gaussian Clustering
• Efficient Greedy Learning of Gaussian Mixture Models
• A Hierarchical Eigenmodel for Pooled Covariance Estimation
• Improved criteria for clustering based on the posterior similarity matrix

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