Abstract
Analogous to the EM algorithm, the Data Augmentation algorithm exploits the simplicity of the posterior distribution of the parameter given the augmented data. In contrast to the EM algorithm, the present goal is obtain the entire posterior distribution, not just the maximizer and the curvature at the maximizer. In large samples, it is comforting that the observed posterior is consistent with the normal approximation, though in practice it is not often clear when one is in a large sample setting. In a small sample situation, the Data Augmentation algorithm will provide a way of improving inference based on the entire posterior distribution.
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© 1991 Springer-Verlag Berlin Heidelberg
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Tanner, M.A. (1991). The Data Augmentation Algorithm. In: Tools for Statistical Inference. Lecture Notes in Statistics, vol 67. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-0510-1_5
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DOI: https://doi.org/10.1007/978-1-4684-0510-1_5
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