ML Anthology

First-Order Bayes-Ball

Wannes Meert, Nima Taghipour, Hendrik Blockeel
ECML-PKDD 2010 pp. 369-384
doi:10.1007/978-3-642-15883-4_24 /ecmlpkdd/2010/meert2010ecmlpkdd-firstorder/

Abstract

Efficient probabilistic inference is key to the success of statistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayes-ball algorithm can identify the requisite variables in a propositional Bayesian network and thus ignore irrelevant variables. This paper presents a lifted version of Bayes-ball, which works directly on the first-order level, and shows how this algorithm applies to (lifted) inference in directed first-order probabilistic models.

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Cite

Text

Meert et al. "First-Order Bayes-Ball." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15883-4_24

Markdown

[Meert et al. "First-Order Bayes-Ball." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/meert2010ecmlpkdd-firstorder/) doi:10.1007/978-3-642-15883-4_24

BibTeX

@inproceedings{meert2010ecmlpkdd-firstorder,
  title     = {{First-Order Bayes-Ball}},
  author    = {Meert, Wannes and Taghipour, Nima and Blockeel, Hendrik},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2010},
  pages     = {369-384},
  doi       = {10.1007/978-3-642-15883-4_24},
  url       = {https://mlanthology.org/ecmlpkdd/2010/meert2010ecmlpkdd-firstorder/}
}