Bayesian Graphical Models for Discrete Data (1993)

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by David Madigan , Jeremy York
Citations:227 - 18 self

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172 A Guide to the Literature on Learning Probabilistic Networks From Data – Wray Buntine - 1996
92 A characterization of Markov equivalence classes for acyclic digraphs – Steen A. Andersson, David Madigan, Michael D. Perlman - 1995
30 On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs – Steen A. Andersson, David Madigan, Michael D. Perlman - 1994
38 Bayesian Model Averaging And Model Selection For Markov Equivalence Classes Of Acyclic Digraphs – David Madigan, Steen Andersson, Michael Perlman, Chris Volinsky - 1996
249 Operations for Learning with Graphical Models – Wray L. Buntine - 1994
1 Learning Belief Networks in Pseudo-independent Domains – Yang Xiang, C Fl Yang Xiang, Ss A, Y. Xiang - 1996
9 A graphical characterization of lattice conditional independence models – Steen A. Andersson, David Madigan, Michael D. Perlman, Christopher M. Triggs - 1997
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
2 Bayesian Networks for Genomic Analysis – Paola Sebastiani, Maria M. Abad, Marco F. Ramoni - 2004
Bayesian AI Tutorial – Kevin B. Korb, Ann E. Nicholson
unknown title – Learning Bayesian
849 A tutorial on learning with Bayesian networks – David Heckerman - 1995
49 An Alternative Markov Property for Chain Graphs – Steen Andersson, David Madigan, Michael Perlman - 1996
167 Probabilistic independence networks for hidden Markov probability models – Padhraic Smyth, David Heckerman, Michael I. Jordan - 1996
37 Improved learning of Bayesian networks – Robert Castelo, Craig Boutilier - 2001
7 Enhancing the Predictive Performance of Bayesian Graphical Models – David Madigan, Fred Hutchinson - 1995
27 Chain Graphs for Learning – Wray Buntine - 1995
11 Score and Information for Recursive Exponential Models with Incomplete Data. – Bo Thiesson
21 Graphical Models for Probabilistic and Causal reasoning – Judea Pearl - 2004