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Exact Evaluation of Marginal Likelihood Integrals

In Bayesian statistics, marginal likelihood integrals are important for model selection. Unfortunately, they are generally difficult to compute. We present algebraic algorithms for evaluating such integrals exactly for small sample sizes, and compare them with some existing approximations. Our methods will be applied to mixtures of discrete independence models, and are available as a library in Maple.