Arnaud Guyader

TL;DR: In this paper, the authors analyze the distribution of the lengths of reactive paths in the limit of small temperature, and compare the theoretical results to numerical re-sults obtained by a Monte Carlo method, namely the multi-level splitting approach.

TL;DR: The so-called SQMC particle method to compute posterior Bayesian law, and a nonparametric analysis of the widespread ABC method are described, from an applied and theoretical point of view.

TL;DR: In this paper, an iterative method for estimating a univariate regression function of bounded variation is proposed and analyzed by combining two classical tools in nonparametric statistics, namely isotonic regression and the estimation of additive models.

TL;DR: This paper investigates the theoretical properties of a recently proposed nonparametric estimator, called the Mutual Nearest Neighbors rule, which estimates the regression function m(x) = E[Y|X = x] as follows; it is proved that this estimator is consistent and that its rate of convergence is optimal.

TL;DR: In this article, a complete framework for determining the asymptotic efficiency of estimators of large deviation probabilities and rate functions based on importance sampling is presented, which relies on the idea that importance sampling in that context is fully characterized by the joint large deviations of two random variables: the observable defining the large deviation probability of interest and the likelihood factor connecting the original process and the modified process used in importance sampling.