MATA271 Stochastic Models (4 cr)
Learning outcomes
After the course, the student:
* knows Markov chains and their properties
* has studied several models where Markov chains are used
* can decide whether a certain real world situation can be modelled by Markov chains
* can analyze Markov chain models and derive properties for the real world situation
* has learned about several Markov Chain Monte Carlo methods.
Study methods
Course exam and exercises. Part of the exercises may be obligatory.
Final exam is an other option.
Content
In this course we study mainly Markov chains. Besides investigating their properties, for example the behavior as time goes to infinity, we consider many applications, among them:
* a simple weather forecast model,
* a discrete-time share price model,
* a model to describe the risk of cancer caused by radiation
* random walk as a special case of a Markov chain
Finally we discuss that Markov Chain Monte Carlo methods work because there is a generalized Law of Large Numbers behind.
Materials
Lecture notes: C. Geiss. Stochastic modeling.
Assessment criteria
The grade is based on
a) the number of points in the course exam and possibly additional points from exercises
OR
b) the number of points in the final exam.
At least half of the points are needed to pass the course.
Prerequisites
MATA280 Foundations of stochastics or TILA121 Probability or TILA1200 Probability 1.