Dependency Networks for Inference, Collaborative Filtering, and Data Visualization

D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie

Abstract

We describe a graphical representation of probabilistic relationships — an alternative to the Bayesian network — called a dependency network. Like a Bayesian network, a dependency network has a graph and probability component. The graph component is a (cyclic) directed graph such that a node's parents render that node independent of all other nodes in the network. The probability component consists of the probability of a node given its parents for each node (as in a Bayesian network). We identify several basic properties of this representation, and describe its use in density estimation, collaborative filtering (the task of predicting preferences), and the visualization of predictive relationships.

Details

Publication typeArticle
Published inJournal of Machine Learning Research
URLhttp://www.ai.mit.edu/projects/jmlr/
Pages49-75
Volume1
NumberMSR-TR-2000-16
InstitutionMicrosoft Research
PublisherJournal of Machine Learning Research
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