Table of contents

The course is connected to the following study programs

Teaching language

Norwegian or English

Recommended previous knowledge

Calculus 1, Calculus 2, Statistics, Linear Algebra, and Real Analysis. A course in Lebesgue Integration is recommended to be taken before or at the same time as this subject.

Learning outcomes

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

  • master some of the most common techniques for analysing 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
  • know what a Poisson process is and know the connection to exponential distributions
  • know some of the most basic properties of discrete Markov chains in continuous time
  • know 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. Estimated workload of the course is 267 hours.

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.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 6:34:51 PM