Malsiner-Walli, Gertraud, Frühwirth-Schnatter, Sylvia, Grün, Bettina. 2019. Telescoping mixtures - Learning the number of components and data clusters in Bayesian mixture analysis. 16th Conference of the International Federation of Classification Societies, Thessaloniki, Griechenland, 26.08.-29.08.
BibTeX
Abstract
Telescoping mixtures are an extension of sparse finite mixtures by assuming that additional to the unknown number of data clusters also the number of mixture components is unknown and has to be estimated. Telescoping mixtures explicitly distinguish between the number of data clusters K+ and components K in the mixture distribution, and purposely allow for more components than data clusters. By linking the prior on the number of components to the prior on the mixture weights, it is guaranteed that components remain empty as K increases, making the number of clusters in the data, defined through the partition implied by the allocation variables, random a priori. Telescoping mixtures can be seen as an alternative to infinite mixtures models. We present a simple algorithm for posterior MCMC sampling to jointly sample K, the number of components, and K+, the number of data clusters. The algorithm is compared to standard transdimensional algorithm such as the reversible jump Markov chain Monte Carlo and the Jain-Neal split-merge sampler.
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Status of publication | Published |
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Affiliation | WU |
Type of publication | Paper presented at an academic conference or symposium |
Language | English |
Title | Telescoping mixtures - Learning the number of components and data clusters in Bayesian mixture analysis |
Event | 16th Conference of the International Federation of Classification Societies |
Year | 2019 |
Date | 26.08.-29.08. |
Country | Greece |
Location | Thessaloniki |
URL | https://ifcs.gr/ |
Associations
- Projects
- Shrinking and Regularizing Finite Mixture Models
- People
- Malsiner-Walli, Gertraud (Details)
- Frühwirth-Schnatter, Sylvia (Details)
- External
- Grün, Bettina (Johannes Kepler Universität Linz, Austria)
- Organization
- Institute for Statistics and Mathematics IN (Details)
- Research areas (ÖSTAT Classification 'Statistik Austria')
- 1162 Statistics (Details)
- 5701 Applied statistics (Details)