Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos

AISTATS 2018 pp. 948-957

doi:10.48550/arXiv.2408.14935 /aistats/2018/silander2018aistats-quotient/

Abstract

We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely related factorized normalized maximum likelihood criterion, qNML satisfies the property of score equivalence. It is also decomposable and completely free of adjustable hyperparameters. For practical computations, we identify a remarkably accurate approximation proposed earlier by Szpankowski and Weinberger. Experiments on both simulated and real data demonstrate that the new criterion leads to parsimonious models with good predictive accuracy.

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Text

Silander et al. "Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures." International Conference on Artificial Intelligence and Statistics, 2018. doi:10.48550/arXiv.2408.14935

Markdown

[Silander et al. "Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/silander2018aistats-quotient/) doi:10.48550/arXiv.2408.14935

BibTeX

@inproceedings{silander2018aistats-quotient,
  title     = {{Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures}},
  author    = {Silander, Tomi and Leppä-aho, Janne and Jääsaari, Elias and Roos, Teemu},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2018},
  pages     = {948-957},
  doi       = {10.48550/arXiv.2408.14935},
  url       = {https://mlanthology.org/aistats/2018/silander2018aistats-quotient/}
}