Arnaud Guillin

Adaptive importance sampling in general mixture classes

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.

Transportation cost-information inequalities and applications to random dynamical systems and diffusions

We first give a characterization of the L1-transportation cost-information inequality on a metric space and next find some appropriate sufficient condition to transportation cost-information inequalities for dependent sequences. Applications to random dynamical systems and diffusions are studied.

On the rate of convergence in Wasserstein distance of the empirical measure

Let

Uniform Convergence to Equilibrium for Granular Media

\mu _N

Moderate deviations for Markov chains with atom

μN be the empirical measure associated to a

Off-Line Detection of Multiple Change Points by the Filtered Derivative with p-Value Method

N