Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations

We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter α . This paper introduces a Gibbs sampling algorithm that combines the slice sampling approach of Walker (Communications in Statistics - Simulatio...

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Veröffentlicht in:	Statistics and computing Jg. 25; H. 5; S. 1023 - 1037
Hauptverfasser:	Hastie, David I., Liverani, Silvia, Richardson, Sylvia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2015
Schlagworte:	Algorithms Artificial Intelligence Computation Computer simulation Dirichlet problem Mathematical models Mathematics and Statistics Probability and Statistics in Computer Science Sampling Source code Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Mixture model Profile regression Dirichlet process Bayesian clustering
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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