250108 VO Stochastic Processes (2008W)
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Details
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 01.10. 09:00 - 11:00 Seminarraum
- Thursday 02.10. 09:00 - 11:00 Seminarraum
- Wednesday 08.10. 09:00 - 11:00 Seminarraum
- Thursday 09.10. 09:00 - 11:00 Seminarraum
- Wednesday 15.10. 09:00 - 11:00 Seminarraum
- Thursday 16.10. 09:00 - 11:00 Seminarraum
- Wednesday 22.10. 09:00 - 11:00 Seminarraum
- Thursday 23.10. 09:00 - 11:00 Seminarraum
- Wednesday 29.10. 09:00 - 11:00 Seminarraum
- Thursday 30.10. 09:00 - 11:00 Seminarraum
- Wednesday 05.11. 09:00 - 11:00 Seminarraum
- Thursday 06.11. 09:00 - 11:00 Seminarraum
- Wednesday 12.11. 09:00 - 11:00 Seminarraum
- Thursday 13.11. 09:00 - 11:00 Seminarraum
- Wednesday 19.11. 09:00 - 11:00 Seminarraum
- Thursday 20.11. 09:00 - 11:00 Seminarraum
- Wednesday 26.11. 09:00 - 11:00 Seminarraum
- Thursday 27.11. 09:00 - 11:00 Seminarraum
- Wednesday 03.12. 09:00 - 11:00 Seminarraum
- Thursday 04.12. 09:00 - 11:00 Seminarraum
- Wednesday 10.12. 09:00 - 11:00 Seminarraum
- Thursday 11.12. 09:00 - 11:00 Seminarraum
- Wednesday 17.12. 09:00 - 11:00 Seminarraum
- Thursday 18.12. 09:00 - 11:00 Seminarraum
- Wednesday 07.01. 09:00 - 11:00 Seminarraum
- Thursday 08.01. 09:00 - 11:00 Seminarraum
- Wednesday 14.01. 09:00 - 11:00 Seminarraum
- Thursday 15.01. 09:00 - 11:00 Seminarraum
- Wednesday 21.01. 09:00 - 11:00 Seminarraum
- Thursday 22.01. 09:00 - 11:00 Seminarraum
- Wednesday 28.01. 09:00 - 11:00 Seminarraum
- Thursday 29.01. 09:00 - 11:00 Seminarraum
Information
Aims, contents and method of the course
A stochastic process with discrete time is a sequence X(n), n=0,1,2,..., of random variables taking values in a set S, the state space. The random variable X(n) represents the state of the system at time n. For example, if a gambler repeatedly plays the same game, and if X(n) is the amount of money the gambler has left at time n, then S={0,1,2,...}. If the gambler repeatedly throws a single die and X(n) is the result of the n-th throw, then one would choose S={1,2,3,4,5,6}, etc.A stochastic process with continuous time is a family of random variables X(t), t?0, with values in a `state space' S. The random variable X(t) again represents the state of the system at time t. As an example one can take the length of the queue at a ticket counter. Since this 'length' is the number of people in the queue, S={0,1,2,...}.By assuming certain regularities in the evolution of this process one obtains special types of stochastic processes, such as Markov processes or renewal processes. Both these classes will be discussed in detail in this course.
Assessment and permitted materials
Mitarbeit in der Vorlesung, mündliche Abschlussprüfung
Minimum requirements and assessment criteria
Introduction to tools and methods of stochastic analysis. Examples and applications of stochastic processes.
Examination topics
Lecture course
Reading list
Karlin: A first course in stochastic processes
Karlin, Taylor: A second course in stochastic processes
Grimmett, Stirzaker: Probability and random processes
Feller: An introduction to probability theory I and II
Karlin, Taylor: A second course in stochastic processes
Grimmett, Stirzaker: Probability and random processes
Feller: An introduction to probability theory I and II
Association in the course directory
MSTP
Last modified: Mo 07.09.2020 15:40