PGM 2006: Prague, Czech Republic

• Joaquín Abellán, Manuel Gómez-Olmedo, Serafín Moral:
  Some Variations on the PC Algorithm. 1-8
  
• Peter Antal, András Gezsi, Gábor Hullám, András Millinghoffer:
  Learning Complex Bayesian Network Features for Classification. 9-16
  
• Peter Antal, András Millinghoffer:
  Literature Mining using Bayesian Networks. 17-24
  
• Alessandro Antonucci, Marco Zaffalon:
  Locally specified credal networks. 25-34
  
• Olav Bangsø, N. Sondberg-Madsen, Finn Verner Jensen:
  A Bayesian Network Framework for the Construction of Virtual Agents with Human-like Behaviour. 35-42
  
• Janneke H. Bolt:
  Loopy Propagation: the Convergence Error in Markov Networks. 43-50
  
• Janneke H. Bolt, Linda C. van der Gaag:
  Preprocessing the MAP Problem. 51-58
  
• Tao Chen, Nevin Lianwen Zhang:
  Quartet-Based Learning of Shallow Latent Variables. 59-66
  
• Barry R. Cobb:
  Continuous Decision MTE Influence Diagrams. 67-74
  
• A. J. Feelders, Jevgenijs Ivanovs:
  Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. 75-82
  
• M. Julia Flores, José A. Gámez, Serafín Moral:
  The Independency tree model: a new approach for clustering and factorisation. 83-90
  
• Olivier François, Philippe Leray:
  Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets. 91-98
  
• Linda C. van der Gaag, Silja Renooij, Petra L. Geenen:
  Lattices for Studying Monotonicity of Bayesian Networks. 99-106
  
• Linda C. van der Gaag, Peter R. de Waal:
  Multi-dimensional Bayesian Network Classifiers. 107-114
  
• José A. Gámez, Juan L. Mateo, Jose Miguel Puerta:
  Dependency networks based classifiers: learning models by using independence. 115-122
  
• José A. Gámez, Rafael Rumí, Antonio Salmerón:
  Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials. 123-130
  
• Marcel van Gerven, Francisco Javier Díez:
  Selecting Strategies for Infinite-Horizon Dynamic LIMIDS. 131-138
  
• Miguel Ángel Gómez-Villegas, Paloma Main, Rosario Susi:
  Sensitivity analysis of extreme inaccuracies in Gaussian Bayesian Networks. 139-146
  
• Christophe Gonzales, N. Jouve:
  Learning Bayesian Networks Structure using Markov Networks. 147-154
  
• Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen:
  Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. 155-162
  
• Rasa Jurgelenaite, Tom Heskes:
  Symmetric Causal Independence Models for Classification. 163-170
  
• Johan Kwisthout, Gerard Tel:
  Complexity Results for Enhanced Qualitative Probabilistic Networks. 171-178
  
• Manuel Luque, Francisco Javier Díez:
  Decision analysis with influence diagrams using Elvira's explanation facilities. 179-186
  
• Irene Martínez, Carmelo Rodríguez, Antonio Salmerón:
  Dynamic importance sampling in Bayesian networks using factorisation of probability trees. 187-194
  
• Stijn Meganck, Sam Maes, Philippe Leray, Bernard Manderick:
  Learning Semi-Markovian Causal Models using Experiments. 195-206
  
• Jason Morton, Lior Pachter, Anne Shiu, Bernd Sturmfels, Oliver Wienand:
  Geometry of rank tests. 207-214
  
• Jens Dalgaard Nielsen, Manfred Jaeger:
  An Empirical Study of Efficiency and Accuracy of Probabilistic Graphical Models. 215-222
  
• Søren Holbech Nielsen, Thomas D. Nielsen:
  Adapting Bayes Network Structures to Non-stationary Domains. 223-230
  
• Kristian G. Olesen, Ole K. Hejlesen, R. Dessau, I. Beltoft, M. Trangeled:
  Diagnosing Lyme disease - Tailoring patient specific Bayesian networks for temporal reasoning. 231-238
  
• D. Ozgur-Unluakin, Taner Bilgiç:
  Predictive Maintenance using Dynamic Probabilistic Networks. 239-246
  
• José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
  Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. 247-254
  
• Silja Renooij, Linda C. van der Gaag:
  Evidence and Scenario Sensitivities in Naive Bayesian Classifiers. 255-262
  
• Alberto Reyes, Pablo H. Ibargüengoytia, Luis Enrique Sucar, Eduardo F. Morales:
  Abstraction and Refinement for Solving Continuous Markov Decision Processes. 263-270
  
• Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
  Bayesian Model Averaging of TAN Models for Clustering. 271-278
  
• Xiaoxun Sun, Marek J. Druzdzel, Changhe Yuan:
  Dynamic Weighting A* Search-based MAP Algorithm for Bayesian Networks. 279-286
  
• Petr Simecek:
  A Short Note on Discrete Representability of Independence Models. 287-292
  
• Peter A. Thwaites, Jim Q. Smith:
  Evaluating Causal effects using Chain Event Graphs. 293-300
  
• Yi Wang, Nevin Lianwen Zhang:
  Severity of Local Maxima for the EM Algorithm: Experiences with Hierarchical Latent Class Models. 301-308
  
• Y. Xiang:
  Optimal Design with Design Networks. 309-316
  
• Changhe Yuan, Marek J. Druzdzel:
  Hybrid Loopy Belief Propagation. 317-324
  
• Adam Zagorecki, Marek J. Druzdzel:
  Probabilistic Independence of Causal Influences. 325-332
  

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