Inhalt des Dokuments
Seminar: Stochastische Modelle in den Neurowissenschaften
LV-Nr. 3236 L 337
Time: Thusedays 14:15
Room: MA 751
Begin: April 16, 2019
The seminar offers perspectives on our current research in the area of stochastic models in neurosciene. The seminar is particularly suitable for BSc and MSc students looking for a final project. Students, who want to obtain a "Seminarschein", are welcome as well.
Termine
| Date | Title | Speaker | Advisor |
|---|---|---|---|
| 16.04. | Parameter Estimation for Stochastic Partial Differential Equations via the Method of Moments | Pasemann | |
| 23.04. | Nonparametric estimation for linear SPDEs from local measurements | Altmeyer | |
| 30.4. | Mean-field limits of interacting nonlinear Hawkes processes | Heesen | |
| 7.5. | [B1] Filtern stochastischer Prozesse in diskreter Zeit, Kapitel 3 aus [F1] | Kurowski/Reimers | |
| 21.5. | [B2] Das Kalman Filter, Kapitel 5 aus [F1] | Reimers/Kurowski | |
| 28.05. | [B3] Das Kalman Filter in stetiger Zeit, Kapitel 6 aus [F1] | Amare | |
| 04.06. | entfällt | ||
11.06. | Zum Kontinuumsgrenzwert von Ensemble Filteralgorithmen in stetiger Zeit | Lange | |
| 18.06. | Stochastische Mean-field Kontrolle | Vogler | |
| 25.06. | fällt aus wegen - ICMNS 2019 Kopenhagen | ||
| 02.07. | [B4] Stabilitaet des Kalman Filters, Kapitel 4 in [F3] | Bauer | |
| 09.07. | [M1] Kontrolle der Fokker-Planck Gleichung, [O1] [M2] Stochastische Optimale Kontrolle mit endlichem Zeithorizont,[O4] | Marquart Schreck |
Literatur
Stochastic Filtering, Data Assimilation:
[F1] B. Fristedt, N, Jain and N. Krylov: Filtering and Prediction: A Primer, Student Mathematical Library, Vol. 38, AMS, 2007
[F2] K. J. H. Law, A. M. Stuart, K. C. Zygalakis: “Data assimilation: a mathematical introduction” homepages.warwick.ac.uk/~masdr/data_assimilation/book_excerpt.pdf
[F3] T. Karvonen: “Stability of linear and non-linear Kalman filters” Master’s thesis users.aalto.fi/~karvont2/
[F4] S. P. Meyn, R. L. Tweedie “Markov chains and stochastic stability”
[F5] Dembo/Zeitouni – Parameter Estimation of Partially Observed Continuous Time Stochastic Processes via the EM algorithm
James/LeGland – Consistent Parameter Estimation for Partially Observed Diffusions with Small Noise https://www.sciencedirect.com/science/article/pii/0304414986900189
https://link.springer.com/content/pdf/10.1007/BF01189903.pdf
[F6] Kalman 1960: A New Approach to Linear Filtering and Prediction Problems; Kalman, Bucy 1961: New Results in Linear Filtering and Prediction Theory www.cs.unc.edu/~welch/kalman/media/pdf/Kalman1960.pdf
[F7] Wonham: On the Separation Theorem of Stochastic Control; Fleming, Rishel: Deterministic and Stochastic Control, Springer 1975: Kapitel 6, Abschnitt 11 epubs.siam.org/doi/pdf/10.1137/0306023
Stochastic Control:
[O1] T. Breiten, K. Kunisch, L. Pfeiffer: Control Strategies for the Fokker-Planck Equation arxiv.org/abs/1707.07510
[O2] L. Pfeiffer: Numerical Methods for Mean-Field-Type Optimal Control Problems, Pure Appl. Funct. Anal. 1 (2016), no. 4, 629–655. arxiv.org/abs/1703.10001
[O3] L. Pfeiffer: Optimality conditions for mean-field type optimal control problems, TU Graz, SFB-Report-2015-015 https://imsc.uni-graz.at/mobis/publications/SFB-Report-2015-015.pdf
[O4] L. Pfeiffer: Two approaches to stochastic optimal control problems with a final-time expectation constraint. Appl. Math. Optim. 77 (2018), no. 2, 377–404. link.springer.com/article/10.1007/s00245-016-9378-9