 | Study programme 2020-2021 | Français | |
 | Stochastic Models for Operational Research | ||
Programme component of Master's in Mathematics à la Faculty of Science |
| Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
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| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-020-M | Optional UE | GILLIS Nicolas | F151 - Mathématique et Recherche opérationnelle |
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 18 | 6 | 0 | 0 | 0 | 6 | 6.00 | 2nd term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-MARO-015 | Stochastic Models of Operations Research | 18 | 6 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component |
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Objectives of Programme's Learning Outcomes
- Have integrated and elaborate mathematical knowledge.
- Mobilise the Bachelor's course in mathematics to address complex issues and have profound mathematical expertise to complement the knowledge developed in the Bachelor's course.
- Use prior knowledge to independently learn high-level mathematics.
- Research mathematical literature in an efficient and relevant way.
- Read research articles in at least one discipline of mathematics.
- Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
- Mobilise knowledge, and research and analyse various information sources to propose innovative solutions targeted unprecedented issues.
- Appropriately make use of computer tools, as required by developing a small programme.
Learning Outcomes of UE
- Model a system using a Markov chain and determine its behavior;
- propose a solution to deal with a queuing system.
Content of UE
In this class, we analyze different stochastic models from operational reasearch. In particular, we focus on Markov chains and their applications (PageRank of Google, Queing systems, etc.).
Prior Experience
Probability and Statistics
Type of Assessment for UE in Q2
- Written examination
Q2 UE Assessment Comments
1 written Examen, 100% within the AA, duration : 3h.
Type of Assessment for UE in Q3
- Written examination
Q3 UE Assessment Comments
1 written Examen, 100% within the AA, duration : 3h.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-MARO-015 |
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Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-MARO-015 |
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Required Reading
| AA | |
|---|---|
| I-MARO-015 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-MARO-015 | Slides |
Recommended Reading
| AA | |
|---|---|
| I-MARO-015 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-MARO-015 | Sans objet |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-MARO-015 | Not applicable |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| I-MARO-015 | Authorized |