ML Anthology

Discovering Structure in Continuous Variables Using Bayesian Networks

Reimar Hofmann, Volker Tresp
NeurIPS 1995 pp. 500-506
/neurips/1995/hofmann1995neurips-discovering/

Abstract

We study Bayesian networks for continuous variables using non(cid:173) linear conditional density estimators. We demonstrate that use(cid:173) ful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on Markov blanket conditional density models.

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Text

Hofmann and Tresp. "Discovering Structure in Continuous Variables Using Bayesian Networks." Neural Information Processing Systems, 1995.

Markdown

[Hofmann and Tresp. "Discovering Structure in Continuous Variables Using Bayesian Networks." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/hofmann1995neurips-discovering/)

BibTeX

@inproceedings{hofmann1995neurips-discovering,
  title     = {{Discovering Structure in Continuous Variables Using Bayesian Networks}},
  author    = {Hofmann, Reimar and Tresp, Volker},
  booktitle = {Neural Information Processing Systems},
  year      = {1995},
  pages     = {500-506},
  url       = {https://mlanthology.org/neurips/1995/hofmann1995neurips-discovering/}
}