Table of Contents

• PrefaceThomas D. Nielsen, Manfred Jaeger. 1-4 [doi]
• Structure Learning from Related Data Sets with a Hierarchical Bayesian ScoreLaura Azzimonti, Giorgio Corani, Marco Scutari. 5-16 [doi]
• Tuning Causal Discovery AlgorithmsKonstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou. 17-28 [doi]
• Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete DataTjebbe Bodewes, Marco Scutari. 29-40 [doi]
• Constraing-Based Learning for Continous-Time Bayesian NetworksAlessandro Bregoli, Marco Scutari, Fabio Stella. 41-52 [doi]
• Sum-Product Network DecompilationCory J. Butz, Jhonatan de S. Oliveira, Robert Peharz. 53-64 [doi]
• Solving Multiple Inference by Minimizing Expected LossCong Chen, Jiaqi Yang, Chao Chen 0012, Changhe Yuan. 65-76 [doi]
• Efficient Heuristic Search for M-Modes InferenceCong Chen, Changhe Yuan, Chao Chen. 77-88 [doi]
• Supervised Learning with Background KnowledgeYizuo Chen, Arthur Choi, Adnan Darwiche. 89-100 [doi]
• Bayesian network structure learning with causal effects in the presence of latent variablesKiattikun Chobtham, Anthony C. Constantinou. 101-112 [doi]
• Approximating bounded tree-width Bayesian network classifiers with OBDDKarine Chubarian, György Turán. 113-124 [doi]
• Gaussian Sum-Product Networks Learning in the Presence of Interval Censored DataPierre Clavier, Olivier Bouaziz, Gregory Nuel. 125-136 [doi]
• Strudel: Learning Structured-Decomposable Probabilistic CircuitsMeihua Dang, Antonio Vergari, Guy Van den Broeck. 137-148 [doi]
• Almost No News on the Complexity of MAP in Bayesian NetworksCassio P. de Campos. 149-160 [doi]
• Contrastive Divergence Learning with Chained Belief PropagationFan Ding, Yexiang Xue. 161-172 [doi]
• An Efficient Low-Rank Tensors Representation for Algorithms in Complex Probabilistic Graphical ModelsGaspard Ducamp, Philippe Bonnard, Anthony Nouy, Pierre-Henri Wuillemin. 173-184 [doi]
• Interactive Anomaly Detection in Mixed Tabular Data using Bayesian NetworksEvan Dufraisse, Philippe Leray, Raphaël Nedellec, Tarek Benkhelif. 185-196 [doi]
• Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk ShippingNils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller 0001. 197-208 [doi]
• Scalable Bayesian Network Structure Learning via Maximum Acyclic SubgraphPierre Gillot, Pekka Parviainen. 209-220 [doi]
• Kernel-based Approach for Learning Causal Graphs from Mixed DataTeny Handhayani, James Cussens. 221-232 [doi]
• Lifted Query Answering in Gaussian Bayesian NetworksMattis Hartwig, Ralf Möller 0001. 233-244 [doi]
• On a possibility of gradual model-learningRadim Jirousek. 245-256 [doi]
• Causal Feature Learning for Utility-Maximizing AgentsDavid Kinney, David Watson. 257-268 [doi]
• Lifted Weight Learning of Markov Logic Networks (Revisited One More Time)Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang 0001. 269-280 [doi]
• Prediction of High Risk of Deviations in Home Care DeliveriesAnders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen, Morten Lindblad, Mads Lause Mogensen, Trine Søby Christensen. 281-292 [doi]
• Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product NetworksDenis Deratani Mauá, Heitor Ribeiro Reis, Gustavo Perez Katague, Alessandro Antonucci 0001. 293-304 [doi]
• Discovering cause-effect relationships in spatial systems with a known direction based on observational dataKonrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes. 305-316 [doi]
• Learning decomposable models by coarseningGeorge Orfanides, Aritz Pérez. 317-328 [doi]
• Correlated Equilibria for Approximate Variational Inference in MRFsLuis E. Ortiz, Boshen Wang, Ze Gong. 329-340 [doi]
• Sum-Product-Transform Networks: Exploiting Symmetries using Invertible TransformationsTomás Pevný, Václav Smídl, Martin Trapp, Ondrej Polácek, Tomás Oberhuber. 341-352 [doi]
• Discriminative Non-Parametric Learning of Arithmetic CircuitsNandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan. 353-364 [doi]
• Learning Optimal Cyclic Causal Graphs from Interventional DataKari Rantanen, Antti Hyttinen, Matti Järvisalo. 365-376 [doi]
• Knowledge Transfer for Learning Markov Equivalence ClassesVerónica Rodríguez-López, Luis Enrique Sucar. 377-388 [doi]
• Differentiable TAN Structure Learning for Bayesian Network ClassifiersWolfgang Roth, Franz Pernkopf. 389-400 [doi]
• Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic ArchitecturesXiaoting Shao, Alejandro Molina 0001, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting. 401-412 [doi]
• A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR RelationsCharupriya Sharma, Zhenyu A. Liao, James Cussens, Peter van Beek. 413-424 [doi]
• A New Perspective on Learning Context-Specific IndependenceYujia Shen, Arthur Choi, Adnan Darwiche. 425-436 [doi]
• Constructing a Chain Event Graph from a Staged TreeAditi Shenvi, Jim Q. Smith. 437-448 [doi]
• Dual Formulation of the Chordal Graph ConjectureMilan Studený, James Cussens, Václav Kratochvíl. 449-460 [doi]
• Bayesian Network Model Averaging Classifiers by SubbaggingShouta Sugahara, Itsuki Aomi, Maomi Ueno. 461-472 [doi]
• Learning Bayesian Networks with Cops and RobbersTopi Talvitie, Pekka Parviainen. 473-484 [doi]
• Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable InferenceNazanin Khosravani Tehrani, Nimar S. Arora, Yucen Lily Li, Kinjal Divesh Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr Masouleh, Eric Lippert, Erik Meijer 0001. 485-496 [doi]
• Missing Values in Multiple Joint Inference of Gaussian Graphical ModelsVeronica Tozzo, Davide Garbarino, Annalisa Barla. 497-508 [doi]
• Building Causal Interaction Models by Recursive UnfoldingLinda C. van der Gaag, Silja Renooij, Alessandro Facchini. 509-520 [doi]
• Poset Representations for Sets of Elementary TripletsLinda C. van der Gaag, Janneke H. Bolt. 521-532 [doi]
• Deep Generalized Convolutional Sum-Product NetworksJos van de Wolfshaar, Andrzej Pronobis. 533-544 [doi]
• Residual Sum-Product NetworksFabrizio Ventola, Karl Stelzner, Alejandro Molina 0001, Kristian Kersting. 545-556 [doi]
• Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature SpacesCen Wan, Alex Alves Freitas. 557-568 [doi]
• Hawkesian Graphical Event ModelsXiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Lingzhou Xue. 569-580 [doi]
• Structural Causal Models Are (Solvable by) Credal NetworksMarco Zaffalon, Alessandro Antonucci 0001, Rafael Cabañas. 581-592 [doi]
• BayesSuites: An Open Web Framework for Visualization of Massive Bayesian NetworksNikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga. 593-596 [doi]
• CREDICI: A Java Library for Causal Inference by Credal NetworksRafael Cabañas, Alessandro Antonucci 0001, David Huber, Marco Zaffalon. 597-600 [doi]
• Probabilistic Graphical Models with Neural Networks in InferPyRafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa. 601-604 [doi]
• GOBNILP: Learning Bayesian network structure with integer programmingJames Cussens. 605-608 [doi]
• aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in PythonGaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin. 609-612 [doi]
• CREMA: A Java Library for Credal Network InferenceDavid Huber, Rafael Cabañas, Alessandro Antonucci 0001, Marco Zaffalon. 613-616 [doi]
• A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University HospitalAnders L. Madsen, Kristian G. Olesen, Jørn Munkhof Møller, Nicolaj Søndberg-Jeppesen, Frank Jensen, Thomas Mulvad Larsen, Per Henriksen, Morten Lindblad, Trine Søby Christensen. 617-620 [doi]
• MeDIL: A Python Package for Causal ModellingAlex Markham, Aditya Chivukula, Moritz Grosse-Wentrup. 621-624 [doi]
• PGM_PyLib: A Toolkit for Probabilistic Graphical Models in PythonJonathan Serrano-Pérez, Luis Enrique Sucar. 625-628 [doi]