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ORCIDInnovations in Bayesian Networks 2008
- Dawn E. Holmes, Lakhmi C. Jain:
Innovations in Bayesian Networks: Theory and Applications. Studies in Computational Intelligence 156, Springer 2008, ISBN 978-3-540-85065-6 - Dawn E. Holmes, Lakhmi C. Jain:
Introduction to Bayesian Networks. 1-5 - Richard E. Neapolitan:
A Polemic for Bayesian Statistics. 7-32 - David Heckerman:
A Tutorial on Learning with Bayesian Networks. 33-82 - Kevin B. Korb, Ann E. Nicholson
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The Causal Interpretation of Bayesian Networks. 83-116 - I. S. P. Daryle Niedermayer:
An Introduction to Bayesian Networks and Their Contemporary Applications. 117-130 - Sylvia B. Nagl, Matthew Williams, Jon Williamson:
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer. 131-167 - Xia Jiang, Michael M. Wagner, Gregory F. Cooper:
Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks. 169-185 - Eitel J. M. Lauría:
An Information-Geometric Approach to Learning Bayesian Network Topologies from Data. 187-217 - Philippe Leray, Stijn Meganck, Sam Maes, Bernard Manderick:
Causal Graphical Models with Latent Variables: Learning and Inference. 219-249 - M. Julia Flores, José A. Gámez, Serafín Moral:
Use of Explanation Treesto Describe the State Space of a Probabilistic-Based Abduction Problem. 251-280 - Dawn E. Holmes:
Toward a Generalized Bayesian Network. 281-288 - Rodrigo de Salvo Braz, Eyal Amir, Dan Roth:
A Survey of First-Order Probabilistic Models. 289-317
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