University of Marne-la-Vallée

About: University of Marne-la-Vallée is a based out in . It is known for research contribution in the topics: Estimator & Context (language use). The organization has 831 authors who have published 1855 publications receiving 55316 citations.

Calculation and selective population of vibrational levels with the Multiconfiguration Time-Dependent Hartree (MCTDH) algorithm

Abstract: In the context of studies of the intramolecular vibrational-energy redistribution (IVR) in molecules with the aid of the multiconfiguration time-dependent Hartree (MCTDH) method, we simulate the dynamics of the selective population of vibrational levels in H 2 CS in the presence of an external time-dependent field. The molecule is described by means of six valence polyspherical coordinates. The potential-energy surface (PES), the dipole moment function, and the kinetic energy are of direct-product form in these coordinates and hence perfectly adapted to MCTDH. In order to obtain the eigenstates and the corresponding transition moments of the system, recent developments of the MCTDH improved relaxation method, for which a first comprehensive description is given here, are exploited.

A mixed finite volume scheme for anisotropic diffusion problems on any grid

Abstract: We present a new finite volume scheme for anisotropic heterogeneous diffusion problems on unstructured irregular grids, which simultaneously gives an approximation of the solution and of its gradient. The approximate solution is shown to converge to the continuous one as the size of the mesh tends to 0, and an error estimate is given. An easy implementation method is then proposed, and the efficiency of the scheme is shown on various types of grids and for various diffusion matrices.

Stochastic versions of the em algorithm: an experimental study in the mixture case

Abstract: We compare three different stochastic versions of the EM algorithm: The Stochastic EM algorithm (SEM), the ''Simulated Annealing'' EM algorithm (SAEM) and the Monte Carlo EM algorithm (MCEM). We focus particularly on the mixture of distributions problem. In this context, we investigate the practical behaviour of these algorithms through intensive Monte Carlo numerical simulations and a real data study. We show that, for some particular mixture situations, the SEM algorithm is almost always preferable to the EM and ''simulated annealing'' versions SAEM and MCEM. For some severely overlapping mixtures, however, none of these algorithms can be confidently used. Then, SEM can be used as an efficient data exploratory tool for locating significant maxima of the likelihood function. In the real data case, we show that the SEM stationary distribution provides a contrasted view of the loglikelihood by emphasizing sensible maxima.