ML Anthology

Marginal Likelihood Integrals for Mixtures of Independence Models

Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu
JMLR 2009 pp. 1611-1631
/jmlr/2009/lin2009jmlr-marginal/

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.

PDF JMLR Semantic Scholar

Cite

Text

Lin et al. "Marginal Likelihood Integrals for Mixtures of Independence Models." Journal of Machine Learning Research, 2009.

Markdown

[Lin et al. "Marginal Likelihood Integrals for Mixtures of Independence Models." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/lin2009jmlr-marginal/)

BibTeX

@article{lin2009jmlr-marginal,
  title     = {{Marginal Likelihood Integrals for Mixtures of Independence Models}},
  author    = {Lin, Shaowei and Sturmfels, Bernd and Xu, Zhiqiang},
  journal   = {Journal of Machine Learning Research},
  year      = {2009},
  pages     = {1611-1631},
  volume    = {10},
  url       = {https://mlanthology.org/jmlr/2009/lin2009jmlr-marginal/}
}