Learning Bayesian Networks: Search Methods and Experimental Results
Abstract
We discuss Bayesian approaches for learning Bayesian networks from data. First, we review a metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994 a, b, c). We see that the metric has a property useful for inferring causation from data. Next, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highestscoring network structures in the special case where every node has at most $k=1$ parent. We show that the general case $(k>1)$ is NP-hard, and review heuristic search algorithms for this general case. Finally, we describe a methodology for evaluating learning algorithms, and use this methodology to evaluate various scoring metrics and search procedures.
Cite
Text
Chickering et al. "Learning Bayesian Networks: Search Methods and Experimental Results." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Chickering et al. "Learning Bayesian Networks: Search Methods and Experimental Results." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/chickering1995aistats-learning/)BibTeX
@inproceedings{chickering1995aistats-learning,
title = {{Learning Bayesian Networks: Search Methods and Experimental Results}},
author = {Chickering, David Maxwell and Geiger, Dan and Heckerman, David},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {112-128},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/chickering1995aistats-learning/}
}