Université Paris-Est Université Paris-Est - Marne-la-Vallée Université Paris-Est - Créteil Val-de-Marne Centre National de la Recherche Scientifique

UPEC

AHMAD Mohd Ali Khameini

Situation: 
Non permanent
Nom: 
AHMAD
Prénom: 
Mohd Ali Khameini
Site: 
Site: 
Statut: 
Équipe de recherche: 
Analyse harmonique et multifractale
Courriel: 
mohd-ali-khameini-bin [dot] ahmad [at] univ-paris-est [dot] fr
Téléphone: 
01 45 17 10 88

Pinning for the membrane model in higher dimensions

Site: 
Date: 
07/03/2017 - 13:45 - 14:45
Salle: 
P2 131
Orateur: 
CIPRIANI Alessandra
Localisation: 
Université de Bath
Localisation: 
Royaume-Uni
Résumé: 

Joint work with E. Bolthausen (University of Zurich) and N. Kurt (TU Berlin).

In this talk we are interested in studying the behavior of a d-dimensional interface around its phase transition. We will specifically discuss the case of a model of "pinning''. This problem arises when one considers an interface rewarded every time it touches the 0-hyperplane. Then there is a competition between attraction to the hyperplane and repulsion due to the decrease of entropy for interfaces pinned at 0. Tuning the strength of the attraction, two behaviors are possible: either energy wins, and the interface stays localized close to 0, or entropy wins, and the interface is repelled away from 0. We will study the effects of pinning for a particular effective interface, the membrane or Bilaplacian model, which is akin to the discrete Gaussian free field. We will draw a parallel between the two models and show how in the membrane case a positive pinning strength localises the field in higher dimensions.

Multi-particle diffusion limited aggregation

Site: 
Date: 
31/01/2017 - 13:45 - 14:45
Salle: 
P2 131
Orateur: 
STAUFFER Alexandre
Localisation: 
Université de Bath
Localisation: 
Royaume-Uni
Résumé: 

We consider a stochastic aggregation model on $\mathbb Z^d$. Start with an infinite collection of particles located at the vertices of the lattice, with at most one particle per vertex, and initially distributed according to the product Bernoulli measure with parameter $\mu\in(0,1)$. In addition, there is an aggregate, which initially consists of only one particle placed at the origin. Non-aggregated particles move as continuous time simple symmetric random walks obeying the exclusion rule, whereas aggregated particles do not move. The aggregate grows indefinitely by attaching particles to its surface whenever a particle attempts to jump onto it. Our main result states that if on $\mathbb Z^d$, $d$ at least $2$, the initial density of particles $\mu$ is large enough, then with positive probability the aggregate grows with positive speed.

This is a joint work with Vladas Sidoravicius.

TBA

Type: 
Type: 
Site: 
En cours depuis: 
01/09/2015
Orateur: 
MELOTTI Paul
Directeur(s): 
DE TILIERE Béatrice
Co-directeur(s): 
BOUTILLIER Cédric
Localisation: 
Université Paris 6
Localisation: 
France

MELOTTI Paul

Situation: 
Non permanent
Nom: 
MELOTTI
Prénom: 
Paul
Site: 
Site: 
Statut: 

SUN Wangru

Situation: 
Non permanent
Nom: 
SUN
Prénom: 
Wangru
Site: 
Site: 
Statut: 
Équipe de recherche: 
Probabilités et statistiques
Courriel: 
wangru [dot] sun [at] u-pec [dot] fr

TBA

Type: 
Type: 
Site: 
En cours depuis: 
01/09/2013
Orateur: 
SUN Wangru
Directeur(s): 
DE TILIERE Béatrice
Co-directeur(s): 
BOUTILLIER Cédric
Localisation: 
Université Paris 6
Localisation: 
France

Hitting time asymptotics for hard-core interaction on finite graphs

Site: 
Date: 
06/12/2016 - 13:45 - 14:45
Salle: 
P2 131
Orateur: 
ZOCCA Alessandro
Localisation: 
CWI
Localisation: 
Pays-Bas
Résumé: 

Motivated by the study of random-access protocols for wireless networks, we consider the hard-core model with Metropolis transition probabilities on finite graphs and investigate the asymptotic behavior of the first hitting time between its stable states in the low-temperature regime. In particular, we develop a novel combinatorial method to show how the order-of-magnitude of this first hitting time depends on the grid sizes and on the boundary conditions for various types of grid graphs. Our analysis also proves the asymptotic exponentiality of the scaled hitting time and yields the mixing time of the process in the low-temperature limit as side-result. In order to derive these results, we extended the model-independent framework for first hitting times known as "pathwise approach'' to allow for more general initial and target states.

Equations de Hamilton-Jacobi stochastiques

Site: 
Date: 
08/11/2016 - 13:45 - 14:45
Salle: 
P2 131
Orateur: 
GASSIAT Paul
Localisation: 
Université Paris Dauphine
Localisation: 
France
Résumé: 

Dans cet exposé, nous considérons des équations paraboliques stochastiques non linéaires de la forme $du = F(t,x,u,Du,D^2 u) dt + H(x,Du) \circ dB_t$. Dans la première partie, j'indiquerai comment on peut donner un sens à ces équations, en suivant notamment les idées introduites par Lions et Souganidis basées sur la théorie des solutions de viscosité. Dans la deuxième partie de mon exposé je décrirai quelques propriétés de ces équations qui diffèrent des équations déterministes similaires, notamment la vitesse de propagation et les effets régularisants du terme de bruit. Cet exposé s'appuie sur des travaux en commun avec P. Friz, B. Gess, P.L. Lions et P. Souganidis.

Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics

Site: 
Date: 
04/10/2016 - 13:45 - 14:45
Salle: 
P2 P43
Orateur: 
ARBEL Julyan
Localisation: 
INRIA
Localisation: 
France
Résumé: 

Joint work with Stefano Favaro (University of Torino) ; Bernardo Nipoti (Trinity College Dublin) ; Yee Whye Teh (University of Oxford)

Given a sample of size $n$ from a population of individuals belonging to different species with unknown proportions, a popular problem of practical interest consists in making inference on the probability $D_{n}(l)$ that the $(n+1)$-th draw coincides with a species with frequency $l$ in the sample, for any $l=0,1,\ldots,n$. This paper contributes to the methodology of Bayesian nonparametric inference for $D_{n}(l)$. Specifically, under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimation of $D_{n}(l)$, and we investigate the large $n$ asymptotic behaviour of such an estimator. Of particular interest are special cases of our results obtained under the specification of the two parameter Poisson--Dirichlet prior and the normalized generalized Gamma prior, which are two of the most commonly used Gibbs-type priors. With respect to these two prior specifications, the proposed results are illustrated through a simulation study and a benchmark Expressed Sequence Tags dataset. To the best our knowledge, this illustration provides the first comparative study between the two parameter Poisson--Dirichlet prior and the normalized generalized Gamma prior in the context of Bayesian nonparemetric inference for $D_{n}(l)$.

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