Stochastic differential equations and probabilistic numerical methods
Goals
This course deals with modelisation using time continous processes. The goal is to present both theoritical and pratical aspects on stochastic differentiale equations. The second part deals with numerical method to simulate stochastic processes. It is more specifically for students of Mathematic, Actuarial and quantitative finance options and Masters. It is requiered to have followed a course on theory of probability (for example the course in S8 in Ecole Centrale de Lyon)
Programme
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Brownian motion; Ito integral, diffusion processes, SDE. - Monte Carlo Method, important sampling, variance reduction
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Simulation de processus aléatoires ((Markov Chain and Euler Scheme) -
MCMC, Metropolis Hasting and Gibbs, Simulated annealing, stochastic gradient
Sustainable development
DD&RS level 1
Activity contextualised through environmentally sustainable development and social responsibility and/or supported by examples, exercises, applications.
Programme elements related to sustainable development goals
Monte-Carlo method, Stochastic differential equations, MCMC algorithm
Study
12h
Course
16h
Responsibles
- Marie-Christophette BLANCHET
- Alexandre SAIDI
- Céline HARTWEG-HELBERT
- Elisabeth MIRONESCU
Language
English
Keywords
Brownian Motion, Martingales, Ito calculus, Numerical simulations, Monte Carlo Markov chain methods