Marginal Likelihood Integrals for Mixtures of Independence Models
Computation
2009-02-13 v2
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.
Cite
@article{arxiv.0805.3602,
title = {Marginal Likelihood Integrals for Mixtures of Independence Models},
author = {Shaowei Lin and Bernd Sturmfels and Zhiqiang Xu},
journal= {arXiv preprint arXiv:0805.3602},
year = {2009}
}
Comments
28 pages. Journal of Machine Learning Research, to appear