2009
JMLR
JMLR 2009
Marginal Likelihood Integrals for Mixtures of Independence Models
Abstract
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. [abs] [ pdf ][ bib ] © JMLR 2009. (edit, beta)
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— Artificial Intelligence and Mathematics & Optimization
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Keyword Pioneer
— algebraic algorithm
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