ML Anthology

Bayesian and Information-Theories Priors for Bayesian Network Parameters

Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald
ECML-PKDD 1998 pp. 89-94
doi:10.1007/BFB0026676 /ecmlpkdd/1998/kontkanen1998ecml-bayesian/

Abstract

We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the different approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks.

PDF ECML-PKDD Semantic Scholar

Cite

Text

Kontkanen et al. "Bayesian and Information-Theories Priors for Bayesian Network Parameters." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026676

Markdown

[Kontkanen et al. "Bayesian and Information-Theories Priors for Bayesian Network Parameters." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/kontkanen1998ecml-bayesian/) doi:10.1007/BFB0026676

BibTeX

@inproceedings{kontkanen1998ecml-bayesian,
  title     = {{Bayesian and Information-Theories Priors for Bayesian Network Parameters}},
  author    = {Kontkanen, Petri and Myllymäki, Petri and Silander, Tomi and Tirri, Henry and Grünwald, Peter},
  booktitle = {European Conference on Machine Learning},
  year      = {1998},
  pages     = {89-94},
  doi       = {10.1007/BFB0026676},
  url       = {https://mlanthology.org/ecmlpkdd/1998/kontkanen1998ecml-bayesian/}
}