E-raamat: Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings

Structural Sensitivity for the Knowledge Engineering of Bayesian
Networks.- A Pairwise Class Interaction Framework for Multilabel
Classification.- From Information to Evidence in a Bayesian Network.-
Learning Gated Bayesian Networks for Algorithmic Trading.- Local Sensitivity
of Bayesian Networks to Multiple Simultaneous Parameter Shifts.- Bayesian
Network Inference Using Marginal Trees.- On SPI-Lazy Evaluation of Influence
Diagrams.- Extended Probability Trees for Probabilistic Graphical Models.-
Mixture of Polynomials Probability Distributions for Grouped Sample Data.-
Trading off Speed and Accuracy in Multilabel Classification.- Robustifying
the Viterbi algorithm.- Extended Tree Augmented Naive Classifier.- Evaluation
of Rules for Coping with Insufficient Data in Constraint-based Search
Algorithms.- Supervised Classification Using Hybrid Probabilistic Decision
Graphs.- Towards a Bayesian Decision Theoretic Analysis of Contextual Effect
Modifiers.- Discrete Bayesian Network Interpretation of the Cox's
Proportional Hazards Model.- Minimizing Relative Entropy in Hierarchical
Predictive Coding.- Treewidth and the Computational Complexity of MAP
Approximations.- Bayesian Networks with Function Nodes.- A New Method for
Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block
Designs.- Equivalences Between Maximum A Posteriori Inference in Bayesian
Networks and Maximum Expected Utility Computation in Influence Diagrams.-
Speeding Up $k$-Neighborhood Local Search in Limited Memory Influence
Diagrams.- Inhibited Effects in CP-logic.- Learning Parameters in Canonical
Models using Weighted Least Squares.- Learning Marginal AMP Chain Graphs
under Faithfulness.- Learning Maximum Weighted (k+1)-order Decomposable
Graphs by Integer Linear Programming.- Multi-label Classification for Tree
and Directed Acyclic Graphs Hierarchies.- Min-BDeu and Max-BDeu Scores for
Learning Bayesian Networks.- Causal Discovery from Databases with Discrete
and ContinuousVariables.- On Expressiveness of the AMP Chain Graph
Interpretation.- Learning Bayesian Network Structures  when Discrete and
Continuous Variables are Present.- Learning Neighborhoods of High Confidence
in Constraint-Based Causal Discovery.- Causal Independence Models for
Continuous Time Bayesian Networks.- Expressive Power of Binary Relevance and
Chain Classifiers Based on Bayesian Networks for Multi-Label Classification.-
An Approximate Tensor-Based Inference Method Applied to the Game of
Minesweeper.- Compression of Bayesian Networks with NIN-AND Tree Modeling.- A
Study of Recently Discovered Equalities about Latent Tree Models using
Inverse Edges.- An Extended MPL-C Model for Bayesian Network Parameter
Learning with Exterior Constraints.