| Detailed information |
| Original study plan |
Bachelor's programme Technical Mathematics 2025W |
| Learning Outcomes |
Competences |
| Students should have a basic knowledge on probability theory
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| Skills |
Knowledge |
- Know basic notions of Markov-Chain with a discrete and continuous time
- Investigate the properties of Markov-Chains with rewards
- Derive iterative solution for Markov sequential decision processes
- Develop the policy-iteration for the solution of optimization problems
- Apply the policy-iteration algorithm to real-life control problems
- Understand the main properties of the sequential decision processes with discounting
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Calculation of the state probabilities of Markov processes, algorithms of dynamic programming, solution of the optimization problem with the help of Markov decision processes
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|
| Criteria for evaluation |
Written exam
|
| Methods |
Slides and blackboard presentation
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| Language |
English and French |
| Study material |
- Lecture notes
- Howard R., Dynamic programming and Markov processes. Wiley Series, 1960.
- Puterman M., L. Markov decision process. Wiley series in Probability and Mathematical Statistics, 1994.
|
| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
TM1WCVOMARK: VO Markov chains (2000S-2022S)
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