Journal of Machine Learning Research, Volume 1

Leslie Pack Kaelbling, Editor

**David Heckerman, heckerma@microsoft.com
David Maxwell Chickering, dmax@microsoft.com
Christopher Meek, meek@microsoft.com
Robert Rounthwaite, robertro@microsoft.com
Carl Kadie, carlk@microsoft.com
Microsoft Research, One Microsoft Way, Redmond, WA 98052 USA**

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.

Keywords: Dependency networks, Bayesian networks, graphical
models, probabilistic inference, data visualization, exploratory data
analysis, collaborative filtering, Gibbs sampling

- Introduction
- Consistent Dependency Networks
- General Dependency Networks
- Collaborative Filtering
- Data Visualization
- Summary and Future Work
- Acknowledgments
- Appendix: Proofs of Theorems
- Bibliography
- About this document ...

Journal of Machine Learning Research, 2000-10-19