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Prerequisites
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Duration
LevelStochastic Models (B-KUL-G0P65B)
Aims
Becoming familiar with stochastic modelling of dependent stochastic variables, practicing practice examples of stochastic models.
Previous knowledge
A basic knowledge of probability theory and statistics is required. Furthermore, the students has to have the necessary basic calculus background to apply the basic knowledge to real cases and examples.
Course material
Syllabus
Is also included in other courses
- Master in de statistiek (Industrial Statistics) 120 ects.
- Master of Statistics (Industrial Statistics) 120 ects.
Activities
4.0 ects. Stochastic Models (Part 1) (B-KUL-G0P66a)
Content
After a number of introductory examples, we recall basic concepts of probability theory and statistics.
This will be followed by an extensive study of Poisson processes with their generalizations like the compound
Poisson process. Applications are given in the context of credit risk and actuarial sciences.
Next, discrete and continuous Markov chains are discussed.
Examples of population problems, waiting line theory, towards insurance and financial engineering are given.
