

From robust tests to robust Bayes-like posterior distribution
De Yannick Baraud


Statistical learning in biological neural networks
De Johannes Schmidt-Hieber
Apparaît dans la collection : Advances in computational statistical physics / Perspectives en physique statistique computationnelle
This talk is devoted to the presentation of algorithms for simulating rare events in a molecular dynamics context, e.g., the simulation of reactive paths. We will consider $\mathbb{R}^d$ as the space of configurations for a given system, where the probability of a specific configuration is given by a Gibbs measure depending on a temperature parameter. The dynamics of the system is given by an overdamped Langevin (or gradient) equation. The problem is to find how the system can evolve from a local minimum of the potential to another, following the above dynamics. After a brief overview of classical Monte Carlo methods, we will expose recent results on adaptive multilevel splitting techniques.