Is infinity that far? A Bayesian nonparametric perspective of finite mixture models
Author:
Affiliation:
1. Department of Economics, Università degli Studi di Bergamo
2. Yong Loo Lin School of Medicine, National University of Singapore
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference50 articles.
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2. Miller, J. W. and Harrison, M. T. (2018). Mixture models with a prior on the number of components. J. Amer. Statist. Assoc. 113 340–356.
3. Rousseau, J. and Mengersen, K. (2011). Asymptotic behaviour of the posterior distribution in overfitted mixture models. J. R. Stat. Soc. Ser. B. Stat. Methodol. 73 689–710.
4. Stephens, M. (2000). Bayesian analysis of mixture models with an unknown number of components—an alternative to reversible jump methods. Ann. Statist. 28 40–74.
5. Frühwirth-Schnatter, S. and Malsiner-Walli, G. (2019). From here to infinity: Sparse finite versus Dirichlet process mixtures in model-based clustering. Adv. Data Anal. Classif. 13 33–64.
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