Institutsseminar/2021-11-12 ZusatzIPD: Unterschied zwischen den Versionen
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Aktuelle Version vom 14. Januar 2022, 15:11 Uhr
| Datum | Freitag, 12. November 2021 | |
|---|---|---|
| Uhrzeit | 11:30 – 11:50 Uhr (Dauer: 20 min) | |
| Ort | ||
| Webkonferenz | https://kit-lecture.zoom.us/j/67744231815 | |
| Vorheriger Termin | Fr 5. November 2021 | |
| Nächster Termin | Fr 12. November 2021 |
Termin in Kalender importieren: iCal (Download)
Vorträge
| Vortragende(r) | Li Mingyi |
|---|---|
| Titel | On the Converge of Monte Carlo Dependency Estimators |
| Vortragstyp | Proposal |
| Betreuer(in) | Edouard Fouché |
| Vortragssprache | |
| Vortragsmodus | |
| Kurzfassung | Estimating dependency is essential for data analysis. For example in biological analysis, knowing the correlation between groups of proteins and genes may help predict genes functions, which makes cure discovery easier.
The recently introduced Monte Carlo Dependency Estimation (MCDE) framework defines the dependency between a set of variables as the expected value of a stochastic process performed on them. In practice, this expected value is approximated with an estimator which iteratively performs a set of Monte Carlo simulations. In this thesis, we propose several alternative estimators to approximate this expected value. They function in a more dynamic way and also leverage information from previous approximation iterations. Using both probability theory and experiments, we show that our new estimators converge much faster than the original one. |
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