Celeux, Gilles, Kamary, Kaniav, Malsiner-Walli, Gertraud, Marin, Jean-Michel, Robert, Christian P. 2019. Computational solutions for Bayesian inference in mixture models. In: Handbook of Mixture Analysis, Hrsg. Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert, 73-96. Boca Raton, Florida: Chapman & Hall.
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Abstract
This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the robustness of the Gibbs sampler in high dimensional Gaussian settings.
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| Status of publication | Published |
|---|---|
| Affiliation | WU |
| Type of publication | Chapter in edited volume |
| Language | English |
| Title | Computational solutions for Bayesian inference in mixture models |
| Title of whole publication | Handbook of Mixture Analysis |
| Editor | Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert |
| Page from | 73 |
| Page to | 96 |
| Location | Boca Raton, Florida |
| Publisher | Chapman & Hall |
| Year | 2019 |
| Reviewed? | Y |
| ISBN | 978-1498763813 |
| Open Access | N |
Associations
- Projects
- Shrinking and Regularizing Finite Mixture Models
- People
- Malsiner-Walli, Gertraud (Details)
- External
- Celeux, Gilles (INRIA Saclay, France)
- Kamary, Kaniav (INRIA Saclay, France)
- Marin, Jean-Michel (Universite´ de Montpellier, France)
- Robert, Christian P. (Universite´ Paris-Dauphine, France)
- Organization
- Institute for Statistics and Mathematics IN (Details)
- Research areas (ÖSTAT Classification 'Statistik Austria')
- 1162 Statistics (Details)
- 5701 Applied statistics (Details)