Discrete Markov Processes, fall 2016
Stochastic processes are probabilistic models for random phenomena that display some forms of dependencies over time or space. Markov processes are the most important subclass of such processes: they are mathematically tractable, yet they have a wide range of applications. The primary goal of the course is to provide an introduction to discrete state Markov processes, notably Bernoulli, Poisson, and Markov processes. The emphasis is on practical applications rather than on theoretical rigor. Illustrations include gambling problems, simple operations research, Markov chain Monte Carlo, and Google’s PageRank. R program may be used in practical calculations.
To pass the course, students will complete 3-4 homework sets that are graded. These form 2/3 of the final grade. In addition, a small (2 hour) final grade is worth 1/3.
The course will be given at the level of Advanced Studies (syventävät opinnot) in Statistics.
A prerequisite for the course is probability theory at the level of Subject Studies. If in doubt, contact the lecturer. Part of the course material comes from Cinlar (1975) Introduction to Stochastic Processes, especially chapters 4-5.
The course will be given in English,
during 1.11.2016 -14.12.2016,
Tuesdays, Wednesdays at 10-12,
in Exactum .
Did you forget to register? Please contact tilasto-info[at]helsinki.fi