Inhalt des Dokuments
Seminar: Stochastische Modelle in den Neurowissenschaften
LV-Nr. 3236 L 337
Prof. Dr. Wilhelm Stannat [1]
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 [2]
[F3] T. Karvonen: “Stability of linear and non-linear Kalman filters” Master’s thesis users.aalto.fi/~karvont2/ [3]
[F4] S. P. Meyn, R. L. Tweedie “Markov chains and stochastic stability”
probability.ca/MT/BOOK.pdf [4]
[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
[5]
https://link.springer.com/content/pdf/10.1007/BF01189903.pdf
[6]
[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 [7]
[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 [8]
Stochastic Control:
[O1] T. Breiten, K. Kunisch, L. Pfeiffer: Control Strategies for the Fokker-Planck Equation arxiv.org/abs/1707.07510 [9]
[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 [10]
[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 [11]
[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 [12]
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89903.pdf
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