TU Berlin

FG Mathematische Stochastik / Stochastische Prozesse in den NeurowissenschaftenSeminar SoSe 2019

Page Content

to Navigation

There is no English translation for this web page.

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”

probability.ca/MT/BOOK.pdf

[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

Navigation

Quick Access

Schnellnavigation zur Seite über Nummerneingabe