Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
Abstract
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the elrpectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.
Cite
Text
Myers et al. "Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms." Conference on Uncertainty in Artificial Intelligence, 1999.Markdown
[Myers et al. "Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms." Conference on Uncertainty in Artificial Intelligence, 1999.](https://mlanthology.org/uai/1999/myers1999uai-learning/)BibTeX
@inproceedings{myers1999uai-learning,
title = {{Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms}},
author = {Myers, James W. and Laskey, Kathryn B. and Levitt, Tod S.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1999},
pages = {476-485},
url = {https://mlanthology.org/uai/1999/myers1999uai-learning/}
}