Marginal Likelihood Integrals for Mixtures of Independence Models

2008/05/23 by Shaowei Lin, Bernd Sturmfels, Lin, Shaowei +3 · 2 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Polynomial and algebraic computation #Tensor decomposition and applications

paper · pdf · doi:10.48550/arxiv.0805.3602

openalex publication_date 2008/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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