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Article: DISCUSSION ARTICLE.(sampling algorithms for latent variables)(Statistical Data Included)
- Article from:
- Journal of Computational & Graphical Statistics
- Article date:
- March 1, 2001
- Author:
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Copyright informationCOPYRIGHT 2001 American Statistical Association. This material is published under license from the publisher through the Gale Group, Farmington Hills, Michigan. All inquiries regarding rights should be directed to the Gale Group. (Hide copyright information)
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The term data augmentation refers to methods for constructing iterative optimization or sampling algorithms via the introduction of unobserved data or latent variables. For deterministic algorithms, the method was popularized in the general statistical community by the seminal article by Dempster, Laird, and Rubin on the EM algorithm for maximizing a likelihood function or, more generally, a posterior density. For stochastic algorithms, the method was popularized in the statistical literature by Tanner and Wong's Data Augmentation algorithm for posterior sampling and in the physics literature by Swendsen and Wang's algorithm for sampling from the Ising and Potts models ...
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