On Bayesian ``central clustering: application to landscape classification of Western Ghats

DOI10.1214/11-AOAS454zbMath1366.62122arXiv1111.7105MaRDI QIDQ652362

Sabyasachi Mukhopadhyay, Kajal Dihidar, Sourabh Bhattacharya

Publication date: 14 December 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.7105

zbMATH Keywords

Bayesian analysiscluster analysisDirichlet processGibbs samplingmassive datamixture analysis

Mathematics Subject Classification ID

Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian problems; characterization of Bayes procedures (62C10)

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Uses Software

• mclust

Cites Work

• Unnamed Item
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• On Bayesian ``central clustering: application to landscape classification of Western Ghats
• Gibbs sampling based Bayesian analysis of mixtures with unknown number of components
• Statistical decision theory and Bayesian analysis. 2nd ed
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• MCLUST: Software for model-based cluster analysis
• A Bayesian analysis of some nonparametric problems
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Estimating normal means with a conjugate style dirichlet process prior
• Bayesian Density Estimation and Inference Using Mixtures
• Modal clustering in a class of product partition models