Stochastic Processes (Markov Chains) - Einzelansicht

The course is based on the book of N. Privault, Understanding Markov Chains: Examples and Applications, 2013.

The book, in form of Lecture Notes, is available at the link: https://www.ntu.edu.sg/home/nprivault/MAS328/MAS328-6.pdf

Additional literature:

• R. Durett, Essentials of stochastic processes, 1999   
• N. Norris, Markov Chains, 1998

Probability 1 and Introduction to Measure Theoretic Probability.

Oral or written exam

Stochastic processes play a central role in many scientific areas. This lecture is thought as an introduction to the theory of time-dependent stochastic processes. In particular we will focus on Markov chains.

Important concepts will be: recurrence and transience, stationary and reversible distributions, convergence towards the uniform distribution. A number of examples are analyzed, in particular models from physics (random walk) or from biology (branching processes).

The first part of the course (lecture and exercises) is held by Dr. Sara Mazzonetto and it will be about Discrete-time Markov Chains. The second part (lecture and exercises) will be held by Dr. Pierre Houdebert and is going to focus more on Continuous-time Markov Chains and applications.

In addition we strongly encourage to attend the seminar Simulation of Stochastic Processes hold by Dr. Peter Keller about applications in numerical mathematics, for instance Monte Carlo Markov Chains methods.

First meeting on Wednesday April 22nd at 16:15 in Zoom. 

The content of the course and organization of the course will be presented.

To see the link to the Zoom meeting, Meeting ID and Password connect to the Moodle page.