This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision | ||
ev:2017:pdm_program [2017/10/20 19:07] konstantin |
ev:2017:pdm_program [2017/10/26 08:48] (current) konstantin |
||
---|---|---|---|
Line 48: | Line 48: | ||
| 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 | |