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The Bayesian Mission Mean (BMM) is\nused to evaluate a question of interest (that is, a mean that\nrandomly selects combination of factor levels based on their\nprobability of occurring instead of averaging over the factor\nlevels, as in the grand mean). Under this construct, in\ncontrast to a Gibbs sampler (or Metropolis-within-Gibbs\nsampler), a two-stage sampling method is required. The nested\nsampler determines the conditional posterior distribution of\nthe model parameters, given Y, and the outside sampler\ndetermines the marginal posterior distribution of Y (also\ncommonly called the predictive distribution for Y). 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