Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
Abstract
We describe a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its parents. We identify several basic properties of this representation and describe a computationally efficient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative filtering (the task of predicting preferences), and the visualization of acausal predictive relationships.
Cite
Text
Heckerman et al. "Dependency Networks for Inference, Collaborative Filtering, and Data Visualization." Journal of Machine Learning Research, 2000.Markdown
[Heckerman et al. "Dependency Networks for Inference, Collaborative Filtering, and Data Visualization." Journal of Machine Learning Research, 2000.](https://mlanthology.org/jmlr/2000/heckerman2000jmlr-dependency/)BibTeX
@article{heckerman2000jmlr-dependency,
title = {{Dependency Networks for Inference, Collaborative Filtering, and Data Visualization}},
author = {Heckerman, David and Chickering, David Maxwell and Meek, Christopher and Rounthwaite, Robert and Kadie, Carl},
journal = {Journal of Machine Learning Research},
year = {2000},
pages = {49-75},
volume = {1},
url = {https://mlanthology.org/jmlr/2000/heckerman2000jmlr-dependency/}
}