A Semi-Parametric Bayesian Model for Randomized Block Designs

Steven MacEachern, Christopher A. Bush
Duke University, Ohio State University

Oct 6 1993

A model is proposed for a Bayesian semi-parametric analysis of randomized block experiments. The model is a hierarchical model in which a Dirichlet process is inserted at the middle stage for the distribution of block effects. The models allows an arbitrary distribution of block effects, and it results in effective estimates of treatment contrasts, block effects and the distribution of block effects. An effective computational strategy is presented for describing the posterior distribution.


Dirichlet process, Gibbs sampler, Hierarchical model


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