ML Anthology

Estimating Dependency Structure as a Hidden Variable

Marina Meila, Michael I. Jordan
NeurIPS 1997 pp. 584-590
/neurips/1997/meila1997neurips-estimating/

Abstract

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors.

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Cite

Text

Meila and Jordan. "Estimating Dependency Structure as a Hidden Variable." Neural Information Processing Systems, 1997.

Markdown

[Meila and Jordan. "Estimating Dependency Structure as a Hidden Variable." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/meila1997neurips-estimating/)

BibTeX

@inproceedings{meila1997neurips-estimating,
  title     = {{Estimating Dependency Structure as a Hidden Variable}},
  author    = {Meila, Marina and Jordan, Michael I.},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {584-590},
  url       = {https://mlanthology.org/neurips/1997/meila1997neurips-estimating/}
}