A fast distance-based approach for determining the number of components in mixtures
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Publication:4457766
DOI10.2307/3315900zbMath1039.62022OpenAlexW2110162240MaRDI QIDQ4457766
Sujit K. Sahu, Russell C. H. Cheng
Publication date: 25 March 2004
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2307/3315900
Markov chain Monte CarloBayes factorGibbs samplermixture modelKullback-Leibler distancereversible jump
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Cites Work
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- Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
- Marginal Likelihood from the Gibbs Output
- A Look at Some Data on the Old Faithful Geyser
- A Predictive Approach to Model Selection
- Practical Bayesian Density Estimation Using Mixtures of Normals
- Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
- Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions
- Computational and Inferential Difficulties with Mixture Posterior Distributions
- Penalized minimum‐distance estimates in finite mixture models