Accepted Papers

The list of accepted papers is here below. Click on the titles to read the papers. Slides are also available for most of the papers.

The proceedings are available here. Download the whole volume here.

  1. Marcus Bendtsen. Regime aware learning.
  2. Marco Benjumeda, Concha Bielza, and Pedro Larrañaga. Learning tractable multidimensional Bayesian network classifiers (slides).
  3. Bence Bolgár and Péter Antal. Bayesian matrix factorization with non-random missing data using informative Gaussian process priors and soft evidences (slides).
  4. Janneke Bolt. Bayesian networks: a combined tuning heuristic (slides).
  5. Marcos Bueno, Arjen Hommersom, Peter Lucas, Sicco Verwer, and Alexis Linard. Learning complex uncertain states changes via asymmetric hidden Markov models: an industrial case (slides).
  6. Cory Butz, Jhonatan Oliveira, André dos Santos, and Anders Madsen. On Bayesian network inference with simple propagation (slides).
  7. Cory Butz, André dos Santos, and Jhonatan Oliveira. Relevant path separation: a faster method for testing independencies in Bayesian networks (slides).
  8. Marco Cattaneo. Conditional probability estimation (slides).
  9. Eunice Yuh-Jie Chen, Arthur Choi, and Adnan Darwiche. On pruning with the MDL score (slides).
  10. Fabio Cozman and Denis Mauá. Probabilistic graphical models specified by probabilistic logic programs: semantics and complexity (slides).
  11. Jasper De Bock. Reintroducing credal networks under epistemic irrelevance (slides).
  12. Eugene Dementiev and Norman Fenton. Bayesian torrent classification by file name and size only (slides).
  13. Nicola Di Mauro, Antonio Vergari, and Floriana Esposito. Multilabel classification with cutset networks (slides).
  14. Eva Endres and Thomas Augustin. Statistical matching of discrete data by Bayesian networks (slides).
  15. Nourhene Ettouzi, Philippe Leray, and Montassar Ben Messaoud. An exact approach to learning probabilistic relational model (slides).
  16. Maxime Gasse and Alex Aussem. Identifying the irreducible disjoint factors of a multivariate probability distribution (slides).
  17. Thomas Geier, Michael Glodek, Georg Layher, Heiko Neumann, Susanne Biundo, and Günther Palm. On stacking probabilistic temporal models with bidirectional information flow.
  18. Christiane Görgen and Jim Q. Smith. A differential approach to causality in staged trees (slides).
  19. Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, and David Danks. Causal discovery from subsampled time series data by constraint optmization (slides).
  20. Priyank Jaini, Abdullah Rashwan, Han Zhao, Yue Liu, Ershad Banijamali, Zhitang Chen, and Pascal Poupart. Online algorithms for sum-product networks with continuous variables (slides).
  21. Kiran Karra and Lamine Milli. Hybrid copula Bayesian networks (slides).
  22. Jidapa Kraisangka and Marek Druzdzel. Making large Cox’s proportional hazard models tractable in Bayesian networks.
  23. Johan Kwisthout. The parameterized complexity of approximate inference in Bayesian networks (slides).
  24. Evangelia Kyrimi and William Marsh. A progressive explanation of inference in “hybrid” Bayesian networks for supporting clinical decision making (slides).
  25. Manxia Liu, Arjen Hommersom, Maarten van der Heijden, and Peter Lucas. Learning parameters of hybrid time Bayesian networks (slides).
  26. Daniel Malinsky and Peter Spirtes. Estimating causal effects with ancestral graph Markov models (slides).
  27. Péter Marx, András Millinghoffer, Gabriella Juhász and Péter Antal. Joint Bayesian modelling of internal dependencies and revant multimorbidities of a heterogeneous disease (slides).
  28. Andrés Masegosa, Ana Martínez, Helge Langseth, Thomas Nielsen, Antonio Salmerón, Darío Ramos-López, and Anders Madsen. d-VMP: distributed variational message passing (slides).
  29. Denis Mauá and Fabio Cozman. The effect of combination functions on the complexity of relational Bayesian networks (slides).
  30. Mazen Melibari, Pascal Poupart, Prashant Doshi, and George Trimponias. Dynamic sum product networks for tractable inference on sequence data (slides).
  31. Carlos Morales and Serafín Moral. Regression methods applied to flight variables for situational awarenes estimation using dynamic Bayesian networks (slides).
  32. Juan Miguel Ogarrio, Peter Spirtes, and Joe Ramsey. A hybrid causal search algorithm for latent variable models (slides).
  33. Pekka Parviainen and Samuel Kaski. Bayesian networks for variable groups (slides).
  34. Jose Peña. Learning acyclic directed mixed graphs from observations and interventions (slides).
  35. Martin Plajner and Jiří Vomlel. Student skill models in adaptive testing (slides).
  36. Darí¬≠o Ramos-López, Antonio Salmerón, Rafael Rumí, Ana Martínez, Thomas Nielsen, Andrés Masegosa, Helge Langseth, and Anders Madsen. Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (slides).
  37. Silja Renooij. Evidence evaluation: a study of likelihoods and independence (slides).
  38. Marco Scutari. An empirical-Bayes score for discrete Bayesian networks (slides).
  39. Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, and Gavin Brown. Estimating mutual information in under-reported variables (slides).
  40. Ross Shachter. Decisions and dependence in influence diagrams.
  41. Ivar Simonsson and Petter Mostad. Exact inference on conditional linear Gamma-Gaussian Bayesian networks (slides).
  42. Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, and Tom Heskes. Computing lower and upper bounds on the probability of causal statements (slides).
  43. Milan Studený and James Cussens. The chordal graph polytope for learning decomposable models (slides).
  44. Priya Krishnan Sundararajan and Ole Mengshoel. A genetic algorithm for learning parameters in Bayesian networks using expectation maximization (slides).
  45. Yi Tan, Prakash Shenoy, Moses Chan, and Paul Romberg. On construction of hybrid logistic regression-naive Bayes model for classification (slides).
  46. Yang Xiang and Qian Jiang. Compressing Bayes net CPTs with persistent leaky causes.