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Data augmentation enlarges a posterior distribution with latent variables whose removal by marginalization recovers the original posterior distribution. Convenient full conditional distributions then permit a Gibbs sampler on the enlarged space.

Marginalization sums or integrates a joint probability distribution over unwanted coordinates to obtain a marginal distribution.