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| Detailed information |
| Original study plan |
Master's programme Industrial Mathematics 2025W |
| Learning Outcomes |
Competences |
| Students are acquainted with basic principles concerning stochastic processes as well as with fundamental techniques for proving and computing in this context as required for advanced courses.
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| Skills |
Knowledge |
- Understand and apply the concept of conditional expectation and its properties ;
- Know fundamental properties of stochastic processes;
- Model time dependent random experients with discrete states using Markov chains and study their properties;
- Know several possibilities to construct the Poisson process;
- Model with the Poisson prcocess, e.g., for application problems in insurance mathematics;
- Understand Gaussian processes, in particular the Wiener process and its properties;
- Comprehend the Markov property and Chapman-Kolmogoroc equation for stochastic processes;
- Investigate stochastic processes for the martingale property;
- Know and understand martingale convergence theorems.
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Conditional expectation, stochastic processes, Markov chains, Poisson process, Wiener process, Markov property, Chapman-Kolmogorov equation, martingale, martingale convergence theorems
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| Criteria for evaluation |
Oral exam
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| Methods |
Blackboard presentation
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| Language |
English |
| Study material |
- Stochastik: Theorie und Anwendungen, D. Meintrup and S. Schäffler
- Brownian motion, R. L. Schilling, L. Partzsch
- Knowing the odds, J.B. Walsh
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| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
TMBPAVOPROZ: VO Stochastic processes (2001W-2022S)
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