Table of contents

The course is connected to the following study programs

  • Industrial Mathematics, Bachelor's Programme

Teaching language

Norwegian or English

Recommended previous knowledge

MA-183, MA-184 and MA-227. MA-309 are recommended to be taken before or together with this subject.

Learning outcomes

Upon successful completion of the course, the student will be able to

  • Master some of the most common techniques for analyzing stochastic processes
  • Have knowledge about the basic properties of Markov chains in discrete time, such as periodicity, recurrence, transience and null recurrence
  • Have an overview of some applications of Markov chains in discrete time
  • Have knowledge of what a Poisson process is and know the connection to exponential distributions
  • Have knowledge of some of the most basic properties of discrete Markov chains in continuous time
  • Have knowledge of what a Brownian motion is and know examples of applications

Contents

Markov chains in discrete and continuous time with applications. The Chapman-Kolmogorov equations. Classification of states. Limits and ergodic theory. Branching processes. Monte-Carlo methods. The exponential distribution and the Poisson process as well as applications.

Teaching and learning methods

Lectures, group work, and mandatory hand-ins. The module is estimated to require about 270 hours of effort.

Examination requirements

Mandatory hand-ins must be approved. See Canvas for details.

Examinations

Written, supervised, and graded exam, 5 hours.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes. Subject to availability or capacity.

Admission for external candidates

No
Last updated from FS (Felles studentsystem) Oct. 15, 2026 8:37:34 AM