ev:2017:pdm_program
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ev:2017:pdm_program [2017/10/20 19:07] konstantin |
ev:2017:pdm_program [2017/10/26 08:48] (current) konstantin |
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| | 12:26 | Kadilar | On the Graphical Markov Models with an Application| | | 12:26 | Kadilar | On the Graphical Markov Models with an Application| | ||
| | 12:43 | Michaud | A stochastic heterogeneous mean-field approximation of agent-based models| | | 12:43 | Michaud | A stochastic heterogeneous mean-field approximation of agent-based models| | ||
| - | | 13:00 | lunch break || | + | | 13:00 | group photo (in front of the lecture hall), then lunch break || |
| | // Working Group 3: Network Inference and Prediction // ||| | | // Working Group 3: Network Inference and Prediction // ||| | ||
| | 14:00 | Elliott | A novel approach to network anomaly detection| | | 14:00 | Elliott | A novel approach to network anomaly detection| | ||
| Line 56: | Line 56: | ||
| | 15:40 | Nurushev | Local inference by penalization method for biclustering model| | | 15:40 | Nurushev | Local inference by penalization method for biclustering model| | ||
| | 16:05 | coffee || | | 16:05 | coffee || | ||
| + | | 16:15 | bus departure for old town of Palma || | ||
| ==== Friday (Oct 27) ==== | ==== Friday (Oct 27) ==== | ||
| ^ time ^ presenting author ^ title ^ | ^ time ^ presenting author ^ title ^ | ||
| - | | 09:00 | Lupparelli | Regression graph models for binary non-independent outcomes | | + | | 09:00 | Lupparelli | Graphical models for sequences of non-independent regressions | |
| | 09:35 | Ibañez-Marcelo | When shape matters: Brain networks studied under a persistent homology view| | | 09:35 | Ibañez-Marcelo | When shape matters: Brain networks studied under a persistent homology view| | ||
| | 10:00 | Fernandez-Gracia | Gromov-Wasserstein distance of complex networks| | | 10:00 | Fernandez-Gracia | Gromov-Wasserstein distance of complex networks| | ||
| | 10:25 | Batagelj | Describing network evolution using probabilistic inductive classes| | | 10:25 | Batagelj | Describing network evolution using probabilistic inductive classes| | ||
| - | | 10:35 | coffee break || | + | | 10:50 | coffee break || |
| - | | 11:05 | Rancoita | Bayesian networks for data imputation in survival tree analysis| | + | | 11:20 | Rancoita | Bayesian networks for data imputation in survival tree analysis| |
| - | | 11:30 | Signorelli |How to integrate gene enrichment analysis with information from gene interaction networks| | + | | 11:45 | Signorelli |How to integrate gene enrichment analysis with information from gene interaction networks| |
| - | | 11:55 | Stadler | Gene Trees, Species Trees, Reconciliation Maps, and Phylogenenies| | + | | 12:10 | Stadler | Gene Trees, Species Trees, Reconciliation Maps, and Phylogenenies| |
| - | | 12:30 | closing, lunch || | + | | 12:45 | closing, lunch || |
| Line 87: | Line 88: | ||
| |//Lee//| A Network Epidemic Model for Online Community Commissioning Data | | |//Lee//| A Network Epidemic Model for Online Community Commissioning Data | | ||
| |Lehmann | ERGMs in neuroimaging - developments and challenges| | |Lehmann | ERGMs in neuroimaging - developments and challenges| | ||
| - | |Leskela | Moment-based parameter estimation in binomial random intersection graph | | + | |//Leskela//| Moment-based parameter estimation in binomial random intersection graph | |
| |Marchetti | Graphoid properties and independence with Credal Networks | | |Marchetti | Graphoid properties and independence with Credal Networks | | ||
| |Min | Fragmentation transitions in a coevolving nonlinear voter model | | |Min | Fragmentation transitions in a coevolving nonlinear voter model | | ||
ev/2017/pdm_program.1508519269.txt.gz · Last modified: 2017/10/20 19:07 by konstantin
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