Probabilistic Graphical Models

This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.

Inhalt

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 Continuous Variables.- 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.