The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Estimation of Finite Mixture Distributions Through Bayesian Sampling
Abstract
SUMMARY: A formal Bayesian analysis of a mixture model usually leads to intractable calculations, since the posterior distribution takes into account all the partitions of the sample. We present approximation methods which evaluate the posterior distribution and Bayes estimators by Gibbs sampling, relying on the missing data structure of the mixture model. The data augmentation method is shown to converge geometrically, since a duality principle transfers properties from the discrete missing data chain to the parameters. The fully conditional Gibbs alternative is shown to be ergodic and geometric convergence is established in the normal case. We also consider non-informative approximations associated with improper priors, assuming that the sample corresponds exactly to a k-component mixture.
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Cites
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
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Monte Carlo sampling methods using Markov chains and their applications.
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Convergence of Probability Measures
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Sampling-based approaches to calculating marginal densities
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The Calculation of Posterior Distributions by Data Augmentation
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Markov Chains for Exploring Posterior Distributions
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Statistical Analysis of Finite Mixture Distributions.
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Cited by
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Variable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
Casella, G., & George, E. I. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475878 · Cited by 2,364
Marginal Likelihood from the Gibbs Output
Chib, S. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476635 · Cited by 1,905
Assessing a mixture model for clustering with the integrated completed likelihood
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Metadata
- Authors
- Diebolt, J., & Robert, C. P.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1994-07-01
- Volume
- 56
- Issue
- 2
- Pages
- 363-375
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W130037070
- Cited by
- 912
- References
- 18
- FWCI
- 11.71
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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