Markov chains and mixing times
7.5 credits cr.
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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.
The course consists of one element.
Instruction is given in the form of lectures and exercise/tutor sessions.
The course is assessed through hand-in assignments and a written exam.
A list of examiners can be found on
ScheduleThe 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 literatureNote that the course literature can be changed up to two months before the start of the course.