Orateur
Louis-Pierre Chaintron
(DMA, ENS)
Description
Stochastic diffusions are widely used to model physical phenomena, the noise being useful to account for average effects which need not being specified. However, the proposed model is always an approximation that cannot exactly reproduce all the features of the real system (mean, variance, higher order moment...).
Starting from the Gibbs conditioning principle, this talk presents a systematic way to constrain the law of a diffusion at each time. A careful regularity analysis is done on the corrected process. Quantitative stability is then investigated when perturbing the constraint, showing the robustness of the correction procedure. This work is a collaboration with Giovanni Conforti and Julien Reygner.
