| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Technische Mathematik 2022W |
| Ziele |
(*)A Markov chain is a mathematical model that is useful in the study of complex systems. The basic concepts of a Markov chain are the state of a system and the transition from one state to another. It is said that a system is in a certain state when random variables that fully describe the system take on the values assigned to that state. A transition of the system from one state to another occurs when the variables that describe the system change their values accordingly. The purpose of this course is to give an analytical structure to a Markov decision problem which at the same time describes the system sufficiently well and is still computationally usable.
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| Lehrinhalte |
(*)- Markov-chain with a discrete time
- Controlled Markov-chain
- Iterative solution for sequential decision processes
- The policy-iteration for the solution of sequential decision processes
- Applications of the policy-iteration algorithm
- The policy-iteration algorithm for the processes with several ergodic classes
- The sequential decision processes with discounting
- Continuous-time Markov-chains
- The controllable continuous-time Markov-chains
- The continuous decision problems
- The continuous decision problems with discounting
- Conclusion
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| Beurteilungskriterien |
(*)Written exam
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| Lehrmethoden |
(*)Slides and blackboard presentation
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| Abhaltungssprache |
English |
| Literatur |
(*)- 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.
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)Until term 2022S known as: TM1WCVOMARK VL Markov chains
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
TM1WCVOMARK: VO Markov-Ketten (2000S-2022S)
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