Table of contents

The course is connected to the following study programs

Teaching language

Norwegian or English

Recommended previous knowledge

Topology and Measure Theory, Stochastic Processes

Learning outcomes

After successful completion of the course the student

  • have knowledge about the key concepts of stochastic analysis.
  • have an understanding for stochastic dynamics and models.
  • know numerical methods for solving stochastic differential equations.
  • know Monte-Carlo methods for numerical approximations for a class of parabolic differential equations.

Contents

Continuous stochastic processes. Brownian motion. Martingales. Itô integral. Martingale representation theorem. Itô formula. Stochastic differential equations; strong and weak solutions and criteria’s for well-posedness. Girsanov’s theorem. The Markov property of diffusions. Representation formulas for a class of parabolic differential equations.

Teaching and learning methods

Lectures, group work, and compulsory hand-ins. Estimated workload of the course is 267 hours.

Examination requirements

Approved compulsory hand-ins. See Canvas for more information.

Examinations

Individual oral exam. Graded assessment.

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.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:05:35 PM