D
Dan Geiger
Researcher at Technion – Israel Institute of Technology
Publications - 165
Citations - 18848
Dan Geiger is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Bayesian network & Graphical model. The author has an hindex of 58, co-authored 165 publications receiving 17763 citations. Previous affiliations of Dan Geiger include Microsoft & University of California, Los Angeles.
Papers
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Journal ArticleDOI
Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
Journal ArticleDOI
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Journal ArticleDOI
Learning Bayesian networks: the combination of knowledge and statistical data
TL;DR: It is shown that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen—aprior network—and a single measure of confidence for that network.
Journal ArticleDOI
Identifying independence in bayesian networks
TL;DR: This article characterizes all independence assertions that logically follow from the topology of a network and develops a linear time algorithm that identifies these assertions and is shown to work for a broad class of nonprobabilistic independencies.
Book ChapterDOI
Learning Gaussian networks
Dan Geiger,David Heckerman +1 more
TL;DR: This work extends traditional statistical approaches for identifying vanishing regression coefficients in that it identifies two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.