Embedded Bayesian Network Classifiers (1997)

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by David Heckerman , Christopher Meek
Citations:8 - 1 self

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175 Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables – David Maxwell Chickering, David Heckerman - 1997
54 Stratified exponential families: Graphical models and model selection – Dan Geiger, David Heckerman, Henry King, Christopher Meek - 2001
23 Models and Selection Criteria for Regression and Classification – David Heckerman, Christopher Meek - 1997
heckermacOJmicrosoft.com – Dan Geiger, David Heckerman, Christopher Meek
46 Asymptotic model selection for directed networks with hidden variables – Dan Geiger, David Heckerman, Christopher Meek - 1996
36 Learning Probabilistic Networks – Paul J Krause - 1998
849 A tutorial on learning with Bayesian networks – David Heckerman - 1995
156 Dependency networks for inference, collaborative filtering, and data visualization – David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie
39 The variable selection problem – Edward I. George - 2000
21 Learning Mixtures of Bayesian Networks – Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman - 1997
57 Bayesian Model Selection and Model Averaging – Larry Wasserman - 1999
4 Generalizing The Derivation Of The Schwarz Information Criterion – Joseph E. Cavanaugh, Andrew A. Neath - 1999
19 Graphical models and exponential families – Dan Geiger, Christopher Meek - 1998
563 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
15 On Discriminative Bayesian Network Classifiers and Logistic Regression – Teemu Roos, Hannes Wettig, Peter Grünwald, Petri Myllymäki, Henry Tirri, Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, Iñaki Inza - 2005
4 Supervised Learning of Bayesian Network Parameters Made Easy – Hannes Wettig, Peter Grünwald, Teemu Roos, Petri Myllymäki, Henry Tirri - 2002
Aspects of the Interface between STatistics and . . . – Matt Whiley - 1999
1 Tree Augmented Classification of Binary Data Minimizing Stochastic Complexity – Mats Gyllenberg, Timo Koski - 2002
3 A Bayesian Local Causal Discovery Framework – Subramani Mani - 2005