An online Bayesian mixture labelling method by minimizing deviance of classification probabilities to reference labels
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Publication:5219230
DOI10.1080/00949655.2012.707201zbMath1453.62557OpenAlexW2004657202MaRDI QIDQ5219230
Publication date: 9 March 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2097/17211
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)
Related Items (3)
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Cites Work
- Model based labeling for mixture models
- Dealing with label switching in mixture models under genuine multimodality
- Interpretation and inference in mixture models: simple MCMC works
- Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
- Bayesian Mixture Labeling and Clustering
- Marginal Likelihood from the Gibbs Output
- Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in the Galaxies
- An Application of the Laplace Method to Finite Mixture Distributions
- Dealing With Label Switching in Mixture Models
- Computational and Inferential Difficulties with Mixture Posterior Distributions
- Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
- Bayesian Density Estimation and Inference Using Mixtures
- Bayesian Mixture Labeling by Highest Posterior Density
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