D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie
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.
|Published in||Journal of Machine Learning Research|
|Publisher||Journal of Machine Learning Research|
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