Markov chains and mixing times
The course treats the theory for discrete-time Markov chains.
Central to the course are stationary distributions and convergence towards the stationary distribution. In particular, focus will lie on so-called mixing times, i.e. the time is takes for a Markov chain to approach the stationary distribution, and methods for estimating these. The theory will be illustrated through applications to card shufflings, random walks, statistical physics and/orgenetics. One or more of the following topics will be treated further in some depth: random walks and electrical networks, algorithmic methods such as MCMC-algorithms, and genetic mutations.
-
Course structure
The course consists of one element.
Teaching format
Instruction is given in the form of lectures and exercise/tutor sessions.
Assessment
The course is assessed through hand-in assignments and a written exam.
Examiner
A list of examiners can be found on
-
Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course. -
More information
New student
During your studiesCourse web
We do not use Athena, you can find our course webpages on kurser.math.su.se.
-
Contact
