Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

DOI10.1080/01621459.2019.1594833zbMath1437.62224OpenAlexW2924427693WikidataQ128198385 ScholiaQ128198385MaRDI QIDQ3304855

Marina Vannucci, Raffaele Argiento, Andrea Cremaschi

Publication date: 3 August 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10852/74258

zbMATH Keywords

clusteringBayesian nonparametricsmixture modelshierarchical models

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items (7)

Asymptotic number of clusters for species sampling sequences with non-diffuse base measure ⋮ The semi-hierarchical Dirichlet process and its application to clustering homogeneous distributions ⋮ Perfect sampling of the posterior in the hierarchical Pitman-Yor process ⋮ Sampling hierarchies of discrete random structures ⋮ Hierarchical normalized completely random measures for robust graphical modeling ⋮ Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process ⋮ Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

Uses Software

• S-PLUS
• MEMSS

Cites Work

• Posterior sampling from \(\epsilon\)-approximation of normalized completely random measure mixtures
• On a class of Bayesian nonparametric estimates: I. Density estimates
• Random partition models with regression on covariates
• Distributional results for means of normalized random measures with independent increments
• Distribution theory for hierarchical processes
• Bayesian prediction with multiple-samples information
• Combinatorial stochastic processes. Ecole d'Eté de Probabilités de Saint-Flour XXXII -- 2002.
• Refinement of the upper bounds of the constants in Lyapunov's theorem
• Hierarchical Dirichlet Processes
• Posterior Analysis for Normalized Random Measures with Independent Increments
• A First Course in Bayesian Statistical Methods
• Mixed-Effects Models in S and S-PLUS
• Gibbs Sampling Methods for Stick-Breaking Priors
• Controlling the Reinforcement in Bayesian Non-Parametric Mixture Models
• Probabilistic Symmetries and Invariance Principles
• MCMC for normalized random measure mixture models
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