论文信息 - DATA AUGMENTATION ALGORITHM FOR GRAPHICAL MODELS WITH MISSING DATA

DATA AUGMENTATION ALGORITHM FOR GRAPHICAL MODELS WITH MISSING DATA

In this paper, we discuss an efficient Bayesian computational method when observed data are incomplete in discrete graphical models. The data augmentation (DA) algorithm of Tanner and Wong (8) is applied to finding the posterior distribution. Utilizing the idea of local computation, it is possible to improve the DA algorithm. We propose a local computation DA (LC-DA) algorithm and evaluate its computational efficiency.

Masahiro Kuroda

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