Stochastic Modelling and Simulation

The course includes methods for simulation of random variables (including the inverse transformation method, rejection sampling and transformation methods), generating functions, Markov processes with discrete and continuous time parameter and discrete and continuous sample space, Poisson processes, birth and death processes, queueing processes, renewal processes, statistical inference for stochastic processes, simulation of stochastic processes, an introduction to diffusion processes.

The course is intended to give basic knowledge on stochastic processes and simulation with respect to time, in particular various types of Markov processes. Through the exercises the students become capable of applying this in calculations.

Lectures, compulsory exercises and project paper(s).
Portfolio assessment is the basis for the grade awarded in the course.
This portfolio comprises a written final examination (50%),
project work (30%) and a final oral examination
in relation to the project and the project lectures (20%).
The results for the
constituent parts are to be given in %-points, while the grade for the
whole portfolio (course grade) is given by the letter grading system.

Retake of examination may be given as an oral examination.