ML Anthology

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

Abhik Shah, Peter Woolf
MLOSS 2009 pp. 159-162
/mloss/2009/shah2009jmlr-python/

Abstract

In this paper, we introduce PEBL, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing.

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Cite

Text

Shah and Woolf. "Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data." Machine Learning Open Source Software, 2009.

Markdown

[Shah and Woolf. "Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data." Machine Learning Open Source Software, 2009.](https://mlanthology.org/mloss/2009/shah2009jmlr-python/)

BibTeX

@article{shah2009jmlr-python,
  title     = {{Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data}},
  author    = {Shah, Abhik and Woolf, Peter},
  journal   = {Machine Learning Open Source Software},
  year      = {2009},
  pages     = {159-162},
  volume    = {10},
  url       = {https://mlanthology.org/mloss/2009/shah2009jmlr-python/}
}