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ORCIDPGM 2014: Utrecht, The Netherlands
- Linda C. van der Gaag, A. J. Feelders:
Probabilistic Graphical Models - 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings. Lecture Notes in Computer Science 8754, Springer 2014, ISBN 978-3-319-11432-3 - David W. Albrecht
, Ann E. Nicholson, Chris Whittle:
Structural Sensitivity for the Knowledge Engineering of Bayesian Networks. 1-16 - Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A Pairwise Class Interaction Framework for Multilabel Classification. 17-32 - Ali Ben Mrad, Véronique Delcroix, Sylvain Piechowiak, Philip Leicester:
From Information to Evidence in a Bayesian Network. 33-48 - Marcus Bendtsen, José M. Peña:
Learning Gated Bayesian Networks for Algorithmic Trading. 49-64 - Janneke H. Bolt, Silja Renooij:
Local Sensitivity of Bayesian Networks to Multiple Simultaneous Parameter Shifts. 65-80 - Cory J. Butz, Jhonatan de S. Oliveira, Anders L. Madsen:
Bayesian Network Inference Using Marginal Trees. 81-96 - Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen:
On SPI-Lazy Evaluation of Influence Diagrams. 97-112 - Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza:
Extended Probability Trees for Probabilistic Graphical Models. 113-128 - Barry R. Cobb:
Mixture of Polynomials Probability Distributions for Grouped Sample Data. 129-144 - Giorgio Corani, Alessandro Antonucci, Denis Deratani Mauá, Sandra Gabaglio:
Trading off Speed and Accuracy in Multilabel Classification. 145-159 - Cedric De Boom, Jasper De Bock, Arthur Van Camp, Gert de Cooman:
Robustifying the Viterbi Algorithm. 160-175 - Cassio P. de Campos, Marco Cuccu, Giorgio Corani, Marco Zaffalon:
Extended Tree Augmented Naive Classifier. 176-189 - Martijn de Jongh, Marek J. Druzdzel:
Evaluation of Rules for Coping with Insufficient Data in Constraint-Based Search Algorithms. 190-205 - Antonio Fernández, Rafael Rumí, José del Sagrado, Antonio Salmerón:
Supervised Classification Using Hybrid Probabilistic Decision Graphs. 206-221 - Gábor Hullám, Peter Antal:
Towards a Bayesian Decision Theoretic Analysis of Contextual Effect Modifiers. 222-237 - Jidapa Kraisangka, Marek J. Druzdzel:
Discrete Bayesian Network Interpretation of the Cox's Proportional Hazards Model. 238-253 - Johan Kwisthout:
Minimizing Relative Entropy in Hierarchical Predictive Coding. 254-270 - Johan Kwisthout:
Treewidth and the Computational Complexity of MAP Approximations. 271-285 - Anders L. Madsen, Frank Jensen, Martin Karlsen, Nicolaj Søndberg-Jeppesen:
Bayesian Networks with Function Nodes. 286-301 - Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen:
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. 302-317 - Denis Deratani Mauá:
Equivalences between Maximum a Posteriori Inference in Bayesian Networks and Maximum Expected Utility Computation in Influence Diagrams. 318-333 - Denis Deratani Mauá, Fábio Gagliardi Cozman:
Speeding Up k-Neighborhood Local Search in Limited Memory Influence Diagrams. 334-349 - Wannes Meert, Joost Vennekens:
Inhibited Effects in CP-Logic. 350-365 - Krzysztof Nowak, Marek J. Druzdzel:
Learning Parameters in Canonical Models Using Weighted Least Squares. 366-381 - José M. Peña:
Learning Marginal AMP Chain Graphs under Faithfulness. 382-395 - Aritz Pérez, Christian Blum, José Antonio Lozano:
Learning Maximum Weighted (k+1)-Order Decomposable Graphs by Integer Linear Programming. 396-408 - Mallinali Ramírez-Corona, Luis Enrique Sucar, Eduardo F. Morales:
Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies. 409-425 - Mauro Scanagatta, Cassio P. de Campos, Marco Zaffalon:
Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks. 426-441 - Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes:
Causal Discovery from Databases with Discrete and Continuous Variables. 442-457 - Dag Sonntag:
On Expressiveness of the AMP Chain Graph Interpretation. 458-470 - Joe Suzuki:
Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present. 471-486 - Sofia Triantafilou, Ioannis Tsamardinos, Anna Roumpelaki:
Learning Neighborhoods of High Confidence in Constraint-Based Causal Discovery. 487-502 - Maarten van der Heijden, Arjen Hommersom:
Causal Independence Models for Continuous Time Bayesian Networks. 503-518 - Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. 519-534 - Jirí Vomlel, Petr Tichavský:
An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper. 535-550 - Yang Xiang, Qing Liu:
Compression of Bayesian Networks with NIN-AND Tree Modeling. 551-566 - Nevin Lianwen Zhang, Xiaofei Wang, Peixian Chen:
A Study of Recently Discovered Equalities about Latent Tree Models Using Inverse Edges. 567-580 - Yun Zhou, Norman E. Fenton, Martin Neil:
An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints. 581-596
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