Table of Contents

• Proceedings of the 9th International Conference on Probabilistic Graphical ModelsVáclav Kratochvíl, Milan Studený. [doi]
• Bayesian Network Classifiers Under the Ensemble PerspectiveJacinto Arias, José A. Gámez 0001, José Miguel Puerta. 1-12 [doi]
• Causal Structure Learning via Temporal Markov NetworksAubrey Barnard, David Page. 13-24 [doi]
• An Order-based Algorithm for Learning Structure of Bayesian NetworksShahab Behjati, Hamid Beigy. 25-36 [doi]
• A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory NetworksIoan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes. 37-48 [doi]
• An Empirical Study of Methods for SPN Learning and InferenceCory J. Butz, Jhonatan de S. Oliveira, André E. dos Santos, André L. Teixeira, Pascal Poupart, Agastya Kalra. 49-60 [doi]
• A partial orthogonalization method for simulating covariance and concentration graph matricesIrene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga. 61-72 [doi]
• Cascading Sum-Product Networks using RobustnessDiarmaid Conaty, Jesús Martínez del Rincon, Cassio Polpo de Campos. 73-84 [doi]
• Markov Random Field MAP as Set PartitioningJames Cussens. 85-96 [doi]
• Parallel Probabilistic Inference by Weighted Model CountingGiso H. Dal, Alfons W. Laarman, Peter J. F. Lucas. 97-108 [doi]
• Parameterized hardness of active inferenceNils Donselaar. 109-120 [doi]
• Structure Learning Under Missing DataAlexander Gain, Ilya Shpitser. 121-132 [doi]
• Structure Learning for Bayesian Networks over Labeled DAGsAntti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander. 133-144 [doi]
• Solving M-Modes in Loopy Graphs Using Tree DecompositionsCong Chen, Changhe Yuan, Ze Ye, Chao Chen. 145-156 [doi]
• On the Relative Expressiveness of Bayesian and Neural NetworksArthur Choi, Adnan Darwiche. 157-168 [doi]
• Instance-Specific Bayesian Network Structure LearningFattaneh Jabbari, Shyam Visweswaran, Gregory F. Cooper. 169-180 [doi]
• Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product NetworksPriyank Jaini, Amur Ghose, Pascal Poupart. 181-192 [doi]
• Finding Minimal Separators in LWF Chain GraphsMohammad Ali Javidian, Marco Valtorta. 193-200 [doi]
• A sum-product algorithm with polynomials for computing exact derivatives of the likelihood in Bayesian networksAlexandra Lefebvre, Grégory Nuel. 201-212 [doi]
• Learning Non-parametric Markov Networks with Mutual InformationJanne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos. 213-224 [doi]
• Bayesian Network Structure Learning with Side ConstraintsAndrew Li, Peter Beek. 225-236 [doi]
• Making Continuous Time Bayesian Networks More FlexibleManxia Liu, Fabio Stella, Arjen Hommersom, Peter J. F. Lucas. 237-248 [doi]
• A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label SetsAlexander Oliver Mader, Jens von Berg, Cristian Lorenz, Carsten Meyer. 249-259 [doi]
• Simple Propagation with Arc-Reversal in Bayesian NetworksAnders L. Madsen, Cory J. Butz, Jhonatan de S. Oliveira, André E. dos Santos. 260-271 [doi]
• Learning Bayesian network classifiers with completed partially directed acyclic graphsBojan Mihaljevic, Concha Bielza, Pedro Larrañaga. 272-283 [doi]
• Consistent Estimation given Missing DataKarthika Mohan, Judea Pearl. 284-295 [doi]
• Intervals of Causal Effects for Learning Causal Graphical ModelsSamuel Antonio Montero-Hernández, Felipe Orihuela-Espina, Luis Enrique Sucar. 296-307 [doi]
• Unifying DAGs and UGsJose M. Peña. 308-319 [doi]
• Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical modelsAritz Pérez, Christian Blum 0001, Jose A. Lozano. 320-331 [doi]
• Sparse Learning in Gaussian Chain Graphs for State Space ModelsLasse Petersen. 332-343 [doi]
• Learning Optimal Causal Graphs with Exact SearchKari Rantanen, Antti Hyttinen, Matti Järvisalo. 344-355 [doi]
• Discriminative Training of Sum-Product Networks by Extended Baum-WelchAbdullah Rashwan, Pascal Poupart, Zhitang Chen. 356-367 [doi]
• Same-Decision Probability: Threshold Robustness and Application to ExplanationSilja Renooij. 368-379 [doi]
• Circular Chain ClassifiersJesús Joel Rivas, Felipe Orihuela-Espina, Luis Enrique Sucar. 380-391 [doi]
• Discrete model-based clustering with overlapping subsets of attributesFernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza. 392-403 [doi]
• Differential networking with path weights in Gaussian treesAlberto Roverato, Robert Castelo. 404-415 [doi]
• Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez. 416-427 [doi]
• Formal Verification of Bayesian Network ClassifiersAndy Shih, Arthur Choi, Adnan Darwiche. 427-438 [doi]
• Exact learning augmented naive Bayes classifierShouta Sugahara, Masaki Uto, Maomi Ueno. 439-450 [doi]
• Finding Optimal Bayesian Networks with Local StructureTopi Talvitie, Ralf Eggeling, Mikko Koivisto. 451-462 [doi]
• Representations of Bayesian networks by low-rank modelsPetr Tichavský, Jirí Vomlel. 463-474 [doi]
• Forward-Backward Splitting for Time-Varying Graphical ModelsFederico Tomasi, Veronica Tozzo, Alessandro Verri, Saverio Salzo. 475-486 [doi]
• A Lattice Representation of Independence RelationsLinda C. van der Gaag, Marco Baioletti, Janneke H. Bolt. 487-498 [doi]
• Naive Bayesian Classifiers with Extreme Probability FeaturesLinda C. van der Gaag, Andrea Capotorti. 499-510 [doi]
• Learning Bayesian Networks by Branching on ConstraintsThijs van Ommen. 511-522 [doi]
• Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical ModelsYang Xiang, Abdulrahman Alshememry. 523-534 [doi]