Dependency Networks for Inference, Collaborative Filtering, and Data Visualization

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

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.

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