

Exploring the High-dimensional Random Landscapes of Data Science (3/3)
By Gérard Ben Arous


Exploring the High-dimensional Random Landscapes of Data Science (1/3)
By Gérard Ben Arous
Appears in collection : CEMRACS 2021: Data Assimilation and Model Reduction in High Dimensional Problems / CEMRACS 2021: Assimilation de données et réduction de modèle pour des problêmes en grande dimension
We illustrate how the Hill relation and the notion of quasi-stationary distribution can be used to analyse the error introduced by many algorithms that have been proposed in the literature, in particular in molecular dynamics, to compute mean reaction times between metastable states for Markov processes. We present in particular how this analysis gives rigorous foundations to methods using splitting algorithms to sample the reactive trajectories.