Applied Stochastic Modeling

2019-2020

Course Objective

During this course you will get acquainted with the most often used
stochastic models and how they are applied in practice. The emphasis is
on the variety of stochastic models (and their analysis) that appear in
practice, rather than an in-depth study of a single-class of models.
During the course you learn to handle such practically motivated
problems as an independent researcher; this means that you:
- are able to determine the appropriate model
- can formulate the problem mathematically correct
- are able to solve the stochastic model
- know how to interpret the outcome.

Course Content

This course deals with a number of stochastic modeling techniques that
are often used in practice. They are motivated by showing the business
context in which they are used. Topics we deal with are: time-dependent
Poisson processes and infinite-server queues, renewal processes and
simulation, birth-death-processes, basic queueing models, and inventory
models. We also repeat and extend certain parts of probability theory.

Teaching Methods

Lecture and instruction.

Method of Assessment

Written examination and two hand-in assignments (one in each period).
The assignment in period 1 is graded as a pass-fail, and the assignment
in period 2 counts for 20%; the written examination counts for 80%. The
weighted average grade should be sufficient, but for the written
examination there is a minimum requirement of a 5.0.
The same grading scheme applies to the resit, unless the grade for the
resit exam is higher than the grade for the handin-assignment in period
2, in which case the grade for the resit is determined entirely by the
resit exam.
There is no resit for the hand-in assignment in period 2. The hand-in
assignment in period 1 may be repaired in case of a fail.

Literature

Lecture notes of Ger Koole (made available via Canvas).
Recommended: H.C. Tijms, A First Course in Stochastic Models, 2003. This
is available as e-book via the VU library (ubvu), free of charge.
Additional material will be announced in due time via Canvas.

Target Audience

mBA, mMath

Recommended background knowledge

Probability theory, Poisson process, Markov chains in continuous time

General Information

Course Code X_400392
Credits 6 EC
Period P1+2
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. R. Bekker
Examiner dr. R. Bekker
Teaching Staff dr. R. Bekker

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: