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Dependency networks for inference, collaborative filtering, and data visualization
 Journal of Machine Learning Research
"... We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of ..."
Abstract

Cited by 159 (10 self)
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We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of conditional distributions, one for each nodegiven its parents. We identify several basic properties of this representation and describe a computationally e cient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative ltering (the task of predicting preferences), and the visualization of acausal predictive relationships.
David Heckerman Microsoft Research Valencia 7 June 1, 2002
, 1999
"... Introduction to graphical models Introduction to graphical models ## Applications without data: Expert systems Applications without data: Expert systems Learning from data Learning from data ## Applications of learning Applications of learning Influence diagrams: Graphical models for Influence ..."
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Introduction to graphical models Introduction to graphical models ## Applications without data: Expert systems Applications without data: Expert systems Learning from data Learning from data ## Applications of learning Applications of learning Influence diagrams: Graphical models for Influence diagrams: Graphical models for decision making and causal reasoning decision making and causal reasoning Undirected Graph (UG; MRF; Markov Network) Undirected Graph (UG; MRF; Markov Network) Directed acyclic graph (DAG; Bayesian Network) Directed acyclic graph (DAG; Bayesian Network) Two popular classes of graphical models Two popular classes of graphical models X 1 X 2 X 3 X 1 X 2 X 3 Other types of graphical models Other types of graphical models X Y Z W Chain graphs: Directed cyclic graphs: Z Y ## Domain: X = (X Domain: X = (X 11 ,..., ,...,X X nn ) ) Graphical model = structure + collection of local Graphical model = structure + collection of local distributions distributions Stru
Autologistic Model for Binary Data
, 1971
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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