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.

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In  Journal of Machine Learning Research

Publisher  Journal of Machine Learning Research
© Copyright 2001, JMLR

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