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#### Contents

J. Abellan, M. Gomez-Olmedo, and S. Moral

Some Variations on the PC Algorithm

P. Antal, A. Gezsi, G. Hullam, and A. Millinghoffer

Learning Complex Bayesian Network Features for Classification

P. Antal and A. Millinghoffer

Literature Mining using Bayesian Networks

A. Antonucci and M. Zaffalon

Locally specified credal networks

O. Bangso, N. Sondberg-Madsen, and F. V. Jensen

A Bayesian Network Framework for the Construction of Virtual Agents with Human-like Behaviour

J. H. Bolt

Loopy Propagation: the Convergence Error in Markov Networks

J. H. Bolt and L. C. van der Gaag

Preprocessing the MAP Problem

T. Chen and N. L. Zhang

Quartet-Based Learning of Shallow Latent Variables

B. R. Cobb

Continuous Decision MTE Influence Diagrams

A. Feelders and J. Ivanovs

Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study

M. Julia Flores, J. A. Gamez, and S. Moral

The Independency tree model: a new approach for clustering and factorisation

O. C. H. Francois and P. Leray

Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets

L. C. van der Gaag, S. Renooij, and P. L. Geenen

Lattices for Studying Monotonicity of Bayesian Networks

L. C. van der Gaag and P. R. de Waal

Multi-dimensional Bayesian Network Classifiers

J. A. Gamez, J. L. Mateo, and J. M. Puerta

Dependency networks based classifiers: learning models by using independence

J. A. Gamez, R. Rumi, and A. Salmeron

Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

M. A. J. van Gerven and F. J. Diez

Selecting Strategies for Infinite-Horizon Dynamic LIMIDS

M. A. Gomez-Villegas, Paloma Main, and R. Susi

Sensitivity analysis of extreme inaccuracies in Gaussian Bayesian Networks

C. Gonzales and N. Jouve

Learning Bayesian Networks Structure using Markov Networks

P. O. Hoyer, S. Shimizu, and A. J. Kerminen

Estimation of linear, non-gaussian causal models in the presence of confounding

latent variables

R. Jurgelenaite and T. Heskes

Symmetric Causal Independence Models for Classification

J. Kwisthout and G. Tel

Complexity Results for Enhanced Qualitative Probabilistic Networks

M. Luque and F. J. Diez

Decision analysis with influence diagrams using Elvira's explanation facilities

I. Martinez, C. Rodriguez, and A. Salmeron

Dynamic importance sampling in Bayesian networks using factorisation of probability trees

S. Meganck, S. Maes, P. Leray, and B. Manderick

Learning Semi-Markovian Causal Models using Experiments

J. Morton, L. Pachter, A. Shiu, B. Sturmfels, and O. Wienand

Geometry of rank tests

J. D. Nielsen and M. Jaeger

An Empirical Study of Efficiency and Accuracy of Probabilistic Graphical Models

S. H. Nielsen and T. D. Nielsen

Adapting Bayes Network Structures to Non-stationary Domains

K. G. Olesen, O. K. Hejlesen, R. Dessau, I. Beltoft, and M. Trangeled

Diagnosing Lyme disease - Tailoring patient specific Bayesian networks for temporal reasoning

D. Ozgur-Unluakin and Taner Bilgic

Predictive Maintenance using Dynamic Probabilistic Networks

J. M. Pena, R. Nilsson, J. Bjorkegren, and J. Tegner

Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity

S. Renooij and L. van der Gaag

Evidence and Scenario Sensitivities in Naive Bayesian Classifiers

A. Reyes, P. Ibarguengoytia, L. E. Sucar, and E. Morales

Abstraction and Refinement for Solving Continuous Markov Decision Processes

G. Santafe, J. A. Lozano, and P. Larranaga

Bayesian Model Averaging of TAN Models for Clustering

X. Sun, M. J. Druzdzel, and C. Yuan

Dynamic Weighting A* Search-based MAP Algorithm for Bayesian Networks

P. Simecek

A Short Note on Discrete Representability of Independence Models

P. Thwaites and J. Smith

Evaluating Causal effects using Chain Event Graphs

Y. Wang and N. L. Zhang

Severity of Local Maxima for the EM Algorithm: Experiences with Hierarchical Latent Class Models

Y. Xiang

Optimal Design with Design Networks

C. Yuan and M. J. Druzdzel

Hybrid Loopy Belief Propagation

A. Zagorecki and M. J. Druzdzel

Probabilistic Independence of Causal Influences