  Inference in Bayesian statistics involves the evaluation of marginal
likelihood integrals. We present algebraic algorithms for computing such
integrals exactly for discrete data of small sample size. Our methods apply to
both uniform priors and Dirichlet priors. The underlying statistical models are
mixtures of independent distributions, or, in geometric language, secant
varieties of Segre-Veronese varieties.
