Stochastic differential equations and data-driven modeling
7.5 ECTS creditsThe course includes the following:
- introduction to measure and integration theory (including the Radon-Nikodym theorem, the Lebesgue integral, stochastic integrals)
- introduction to probability theory
- diffusion processes (including Markov processes, Chapman-Enskog processes, ergodicity)
- introduction to stochastic differential equations (SDE), including the Girsanov theorem
- the Fokker-Planck equation
- the Langevin equation
- modeling with SDE (including numerical approximation and parameter estimation for SDE)
- linear response theory (including the Fluctuation-Dissipation theorem, the Green-Kubo formula)
- introduction to measure and integration theory (including the Radon-Nikodym theorem, the Lebesgue integral, stochastic integrals)
- introduction to probability theory
- diffusion processes (including Markov processes, Chapman-Enskog processes, ergodicity)
- introduction to stochastic differential equations (SDE), including the Girsanov theorem
- the Fokker-Planck equation
- the Langevin equation
- modeling with SDE (including numerical approximation and parameter estimation for SDE)
- linear response theory (including the Fluctuation-Dissipation theorem, the Green-Kubo formula)
Progressive specialisation:
A1N (has only first鈥恈ycle course/s as entry requirements)
Education level:
Master's level
Admission requirements
90 ECTS credits in Mathematics, including at least 30 ECTS credits at the G2F level, and English 6 or B, or equivalent
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.
Course code:
MAAD29
The course is not included in the course offerings for the next period.