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Clustering Milky Way's Globulars: a Bayesian Nonparametric Approach

Abstract

This chapter presents a Bayesian nonparametric approach to clustering , which is particularly relevant when the number of components in the clustering is unknown. The approach is illustrated with the Milky Way's glob-ulars, that are clouds of stars orbiting in our galaxy. Clustering globulars is key for better understanding the Milky Way's history. We define the Dirichlet process and illustrate some alternative definitions such as the Chinese restaurant process, the Pólya Urn, the Ewens sampling formula, the stick-breaking representation through some simple R code. The Dirichlet process mixture model is presented, as well as the R package BNPmix implementing Markov chain Monte Carlo sampling. Inference for the clustering is done with the variation of information loss function.
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Dates and versions

hal-01950656 , version 1 (11-12-2018)

Identifiers

  • HAL Id : hal-01950656 , version 1

Cite

Julyan Arbel. Clustering Milky Way's Globulars: a Bayesian Nonparametric Approach. Statistics for Astrophysics: Bayesian Methodology, pp.113-137, 2018. ⟨hal-01950656⟩
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