MATS254 Stochastic processes (4 cr)

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Study level:

Advanced studies

Grading scale:

0-5

Language:

English

Responsible organisation:

Department of Mathematics and Statistics

Curriculum periods:

2017-2018, 2018-2019, 2019-2020

Description

Content

The course gives an introduction into the theory of martingales and some applications. Martingales are one of
the most important classes of stochastic processes. They are widely used in stochastic modelling and in pure mathematics itself. The content of the course is:
* martingales
* Doob's optional stopping theorem
* Doob's martingale convergence theorem
* applications (Branching Processes and Kakutani's Dichotomy Theorem)

Completion methods

Course exam and exercises. Part of the exercises may be obligatory.

Final exam is an other option.

Assessment details

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.

Learning outcomes

After completion of the course, the student
* can calculate conditional expectations
* can decide whether a stochastic process is a martingale
* knows the basic conditions under which a martingale converges
* can apply martingales in stochastic modelling

Description of prerequisites

MATA280 Foundations of stochastics

Recommended: Measure theoretic foundation of probability
(MATS260 Probability 1 or MATS112 Measure and Integration Theory 2)

Study materials

Lecture notes: S. Geiss. Stochastic processes in discrete time

Literature

D. Williams. Probability with martingales, 1991, Cambridge Mathematical Textbooks; ISBN: 978-0521406055

Completion methods

Method 1

Select all marked parts

Method 2

Select all marked parts

Parts of the completion methods

Unpublished assessment item