Bayesian Network Learning with Parameter Constraints
Abstract
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data. This paper considers a variety of types of domain knowledge for constraining parameter estimates when learning Bayesian networks. In particular, we consider domain knowledge that constrains the values or relationships among subsets of parameters in a Bayesian network with known structure.
Cite
Text
Niculescu et al. "Bayesian Network Learning with Parameter Constraints." Journal of Machine Learning Research, 2006.Markdown
[Niculescu et al. "Bayesian Network Learning with Parameter Constraints." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/niculescu2006jmlr-bayesian/)BibTeX
@article{niculescu2006jmlr-bayesian,
title = {{Bayesian Network Learning with Parameter Constraints}},
author = {Niculescu, Radu Stefan and Mitchell, Tom M. and Rao, R. Bharat},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {1357-1383},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/niculescu2006jmlr-bayesian/}
}