Technical Programme
Download the brochure with the complete program.Wednesday, September 19
08:00-08:40 Registration
08:40-09:00 Opening
09:00-10:20 Plenary session 1: Learning I
Chair: Milan Studený10:20-10:50 Coffee break
- J.M. Peña: Learning AMP Chain Graphs under Faithfulness
- J. Suzuki: The Bayesian Chow-Liu Algorithm
- S. Kirshner: Latent Tree Copulas
- D. Sonntag and J.M. Peña: Learning Multivariate Regression Chain Graphs under Faithfulness
10:50-11:50 Invited Talk
Chair:11:50-12:00 Short break
- C. Bielza and P. Larrañaga: Bayesian Networks in Neuroscience
12:00-13:20 Plenary session 2: Modeling and Analysis I
Chair: Peter J.F. Lucas
13:20-14:50 Lunch
- S. Renooij: Generalised Co-Variation for Sensitivity Analysis in Bayesian Networks
- A.R. Masegosa and S. Moral: An Interactive Approach for Cleaning Noisy Observations in Bayesian Networks with the Help of an Expert
- G. Hullam and P. Antal: Estimation of Effect Size Posterior Using Model Averaging over Bayesian Network Structures and Parameters
- G. Borboudakis S. Triantafillou and I. Tsamardinos: Tools and Algorithms for Causally Interpreting Directed Edges in Maximal Ancestral Graphs
14:50-15:50 Plenary session 3: Learning II
Chair: Antonio Salmerón
15:50-16:00 Short break
- M. Studený: Integer Linear Programming Approach to Learning Bayesian Network Structure: towards the Essential Graph
- M. Ueno and M. Uto: Non-Informative Dirichlet Score for Learning Bayesian Networks
- F. Schnitzler, O. Geurts and L. Wehenkel: Mixtures of Bagged Markov Tree Ensembles
16:00-16:40 Plenary session 4: Tools
Chair: Anders L. Madsen
16:40-17:50 Coffee and poster session
- M. Arias, F.J. Díez, M. Palacios-Alonso and I. Bermejo: ProbModelXML: a Format for Encoding Probabilistic Graphical Models
- S. Evers and P.J.F. Lucas: A Framework for Development, Teaching and Deployment of Inference Algorithms
20:00-21:40 Cocktail (Carmen de los Mártires)
22:00- : Night visit to Alhambra
Thursday, September 20
08:00-09:00 Registration
09:00-10:20 Plenary session 5: Classification and Clustering
Chair: Concha Bielza
10:20-10:50 Coffee
- V. Bellón, J. Cerquides and I. Grosse: Gaussian Join Tree Classifiers with Applications to Mass Spectra Classification
- P. Hernandez-Leal, F. Orihuela-Espina, L.E. Sucar and E.F. Morales: Hybrid Binary-Chain Multi-Label Classifiers
- M.J. Flores, J.A. Gámez and A.M. Martínez: Meta-Prediction of Semi-Naive Bayesian Network Classifiers based on Dataset Complexity Characterization
- T. Liu, N.L. Zhang, K.M. Poon, H. Liu and Y. Wang: A Novel LTM-based Method for Multi-Partition Clustering
10:50-11:50 Invited Talk
Chair:
- Norman Fenton: Improving Legal Reasoning with Bayesian Networks
11:50-12:00 Short break
12:00-13:20 Plenary session 6: Modeling and Analysis II
Chair:
13:20-14:50 Lunch
- M. Lappenschaar, A. Hommersom and P.J.F. Lucas: Qualitative Chain Graphs and their Use in Medicine
- B.R. Cobb, R. Rumí and A. Salmerón: Approximating the Distribution of a Sum of Log-Normal Random Variables
- N. Shiers and J.Q. Smith: Graphical Inequality Diagnostics for Phylogenetic Trees
- B.R. Cobb abd P.P. Shenoy: Piecewise Linear Approximations of Non-Linear Deterministic Conditionals in Continuous Bayesian Networks
14:50-16:10 Plenary session 7: Applications
Chair: Philippe Leray
16:10-16:40 Coffee break
- G. Corani, C. Magli, A. Giusti, L. Gianaroli and L. Gambardella: A Bayesian Network Model for Predicting the Outcome of in Vitro Fertilization
- C.B. Pérez-Ariza, A.E. Nicholson and M.J. Flores: Prediction of Coffee Rust Disease Using Bayesian Networks
- K. Vogel, C. Riggelsen, B. Merz, H. Kreibich and F. Scherbaum: Flood Damage and Influencing Factors: A Bayesian Network Perspective
- A. Lupinska-Dubicka and M. Druzdzel: A Comparison of Popular Fertility Awareness Methods to a DBN Model of the Woman's Monthly Cycle
16:40-18:00 Plenary session 8: Learning III
Chair: Marek J. Druzdzel
18:00-19:20 Poster session
- A. Hauser and P. Bühlmann: Two Optimal Strategies for Active Learning of Causal Models from Interventions
- A. Antonucci, G. Corani and S. Gabaglio: Active Learning by the Naive Credal Classifier
- I. Bermejo, J. Oliva, F.J. Díez and M. Arias: Interactive Learning of Bayesian Networks using OpenMarkov
- B. Malone and C. Yuan: A Bounded Error, Anytime, Parallel Algorithm for Exact Bayesian Network Structure Learning
21.30 - Banquet dinner (Santa Paula Hotel)
Friday, September 21
08:00-09:00 Registration
09:00-10:20 Plenary session 9: Learning IV
Chair: José A. Gámez
10:20-10:50 Coffee
- J. Vandel, B. Mangin and S. de Givry: New Local Move Operators for Bayesian Network Structure Learning
- I. Tur and R. Castelo: Learning High-Dimensional Mixed Graphical Models with Missing values
- P.L. López-Cruz, C. Bielza and P. Larrañaga: Learning Mixtures of Polynomials from Data Using B-Spline Interpolation
- H. Langseth, T.D. Nielsen and A. Salmerón: Learning Mixtures of Truncated Basis Functions from Data
10:50-11:50 Plenary session 10: Inference I
Chair: Barry R. Cobb
11:50-12:00 Short break
- H. Langseth, T.D. Nielsen, R. Rumí and A. Salmerón: Inference in Hybrid Bayesian Networks with Mixtures of Truncated Basis Functions
- R. Rumí, A. Salmerón and P.P. Shenoy: Tractable Inference in Hybrid Bayesian Networks with Deterministic Conditionals Using Re-Approximations
- Y. Xiang: Bayesian Network Inference with NIN-AND Tree Models
12:00-13:20 Plenary session 11: Decision Making
Chair: Finn V. Jensen
13:20-14:50 Lunch
- S. Chen, A. Choi and A. Darwiche: The Same-Decision Probability: A New Tool for Decision Making
- R. Cabañas de Paz, M. Gómez-Olmedo and A. Cano: Approximate Inference in Influence Diagrams Using Binary Trees
- V. Lín: Decision-Theoretic Troubleshooting: Hardness of Approximation
- F.J. Díez, M. Luque and C. König: Decision Analysis Networks
14:50-15:50 Plenary session 12: Inference II
Chair: Jirka Vomlel
15:50-16:00 Short break
- M. van der Heijden and P.J.F. Lucas: Probabilistic Reasoning with Temporal Indeterminacy
- R. Eggeling, P-Y. Bourguignon, A. Gohr and I. Grosse: Gibbs Sampling for Parsimonious Markov Models with Latent Variables
- P. Groot and P.J.F. Lucas: Gaussian Process Regression with Censored Data Using Expectation Propagation
16:00-17:00 Plenary session 13: Inference III
Chair: Prakash P. Shenoy
17:00-18:30 Coffee and poster session
- J. Vomlel and P. Tichavský: Computationally Efficient Probabilistic Inference with Noisy Threshold Models based on a CP Tensor Decomposition
- A.L. Madsen a C.J. Butz: On the Importance of Elimination Heuristics in Lazy Propagation
- C. Li and M. Ueno: A Depth-First Search Algorithm for Optimal Triangulation of Bayesian Networks
18:30-19:30 Business meeting and closure