output
19932026
most citedInferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation

688 citations

researchers with a paper here
collaborating institutions
Showing 2017 · math.PRShow all

9 papers · 2 filters

math.PR2017★ 1 cited

Second order BSDE under monotonicity condition and liquidation problem under uncertainty *

Alexandre Popier, Chao Zhou

In this work we investigate an optimal closure problem under Knightian uncertainty. We obtain the value function and an optimal control as the minimal (super-)solution of a second…

math.PR2017★ 1 cited

Directed, cylindric and radial Brownian webs

David Coupier, Jean-François Marckert, Viet Chi Tran

The Brownian web (BW) is a collection of coalescing Brownian paths indexed by the plane. It appears in particular as continuous limit of various discrete models of directed forests…

math.PR2017

Mod-Gaussian convergence for random determinants and random characteristic polynomials

Martina Dal Borgo, Emma Hovhannisyan, Alain Rouault

The aim of this paper is to give a precise asymptotic description of some eigenvalue statistics stemming from random matrix theory. More precisely, we consider random determinants…

math.PR2017★ 4 cited

Propagation of epistemic uncertainty in queueing models with unreliable server using chaos expansions

Katia Bachi, Cédric Chauvière, Hacène Djellout +1

In this paper, we develop a numerical approach based on Chaos expansions to analyze the sensitivity and the propagation of epistemic uncertainty through a queueing systems with bre…

math.PR2017★ 2 cited

Perpetual integrals convergence and extinctions in population dynamics

Camille Coron, Sylvie Méléard, Denis Villemonais

In this article we use a criterion for the integrability of paths of one-dimensional diffusion processes from which we derive new insights on allelic fixation in several situations…

math.PR2017★ 13 cited

Couplings and quantitative contraction rates for Langevin dynamics

Andreas Eberle, Arnaud Guillin, Raphael Zimmer

We introduce a new probabilistic approach to quantify convergence to equilibrium for (kinetic) Langevin processes. In contrast to previous analytic approaches that focus on the ass…