Innovations in Bayesian Networks 2008

• 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:
  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, Matt 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