Rare event simulation

This paper deals with estimations of probabilities of rare events using fast simulation based on the splitting method. In this technique, the sample paths are split into multiple copies at various stages in the simulation. Our aim is to optimize the algorithm and to obtain a precise confidence interval of the estimator using branching processes. The numerical results presented suggest that the method is reasonably efficient

Effective branching splitting method under cost contraint

This paper deals with the splitting method first introduced in rare event analysis. In this technique, the sample paths are split into R multiple copies at various stages during the simulation. Given the cost, the optimization of the algorithm suggests to sample a number of subtrials which may be non-integer and even unknown but estimated. In this paper, we present three different approaches to face this problem which provide precise estimates of the relative error between P(A) and its estimator

A Two-Step branching splitting model under cost constraint for rare event analysis

This paper deals with the splitting method first introduced in rare event analysis. In this technique, the sample paths are split into R multiple copies at various stages to speed up simulation. Given the cost, the optimization of the algorithm suggests to take all the transition probabilities equal; nevertheless, in practice, these quantities are unknown. In this paper, we present an algorithm in two steps that copes with that problem

Deviation results for Mandelbrot's multiplicative cascades with exponential tails

Let $W$ be a nonnegative random variable with expectation $1$. For all $r \geqslant 2$, we consider the total mass $Z_r^\infty$ of the associated Mandelbrot multiplicative cascade in the $r$-ary tree. For all $n \geqslant 1$, we also consider the total mass $Z_r^n$ of the measure at height $n$ in the $r$-ary tree. Liu, Rio, Rouault \cite{lrr,liu2000limit,Rouault04} established large deviation results for $(Z_r^n)_{r \geqslant 2}$ for all $n \in [[1,\infty[[$ (resp., for $n = \infty$) in case $W$ has an everywhere finite cumulant generating function $\Lambda_W$ (resp., $W$ is bounded). Here, we extend these results to the case where $\Lambda_W$ is only finite on a neighborhood of zero. And we establish all deviation results (moderate, large, and very large deviations). It is noticeable that we obtain nonconvex rate functions. Moreover, our proof of upper bounds of deviations for $(Z_r^\infty)_{r \geqslant 2}$ rely on the moment bound instead of the standard Chernoff bound

Analyse des modeles de branchement avec duplication des trajectoires pour l'étude des événements rares

This thesis deals with the splitting method first introduced in rare event analysis in order to speed-up simulation. In this technique, the sample paths are split into $R$ multiple copies at various stages during the simulation. Given the cost, the optimization of the algorithm suggests to take the transition probabilities between stages equal to some constant and to resample the inverse of that constant subtrials, which may be non-integer and even unknown but estimated. First, we study the sensitivity of the relative error between the probability of interest $\mathbb{P}(A)$ and its estimator depending on the strategy that makes the resampling numbers integers. Then, since in practice the transition probabilities are generally unknown (and so the optimal resampling umbers), we propose a two-steps algorithm to face that problem. Several numerical applications and comparisons with other models are proposed.Nous étudions, dans cette thèse, le modèle de branchement avec duplication des trajectoires d'abord introduit pour l'étude des événements rares destiné à accélérer la simulation. Dans cette technique, les échantillons sont dupliqués en $R$ copies à différents niveaux pendant la simulation. L'optimisation de l'algorithme à coût fixé suggère de prendre les probabilités de transition entre les niveaux égales à une constante et de dupliquer un nombre égal à l'inverse de cette constante, nombre qui peut être non entier. Nous étudions d'abord la sensibilité de l'erreur relative entre la probabilité d'intérêt $\mathbb{P}(A)$ et son estimateur en fonction de la stratégie adoptée pour avoir des nombres de retirage entiers. Ensuite, puisqu'en pratique les probabilités de transition sont généralement inconnues (et de même pour les nombres de retirages), nous proposons un algorithme en deux étapes pour contourner ce problème. Des applications numériques et comparaisons avec d'autres modèles sont proposés

Sensitivity analysis for multidimensional and functional outputs

International audienceLet $X:=(X_1, \ldots, X_p)$ be random objects (the inputs), defined on some probability space $(\Omega,{\mathcal{F}}, \mathbb P)$ and valued in some measurable space $E=E_1\times\ldots \times E_p$. Further, let $Y:=Y = f(X_1, \ldots, X_p)$ be the output. Here, $f$ is a measurable function from $E$ to some Hilbert space $\mathbb{H}$ ($\mathbb{H}$ could be either of finite or infinite dimension). In this work, we give a natural generalization of the Sobol indices (that are classically defined when $Y\in\R$ ), when the output belongs to $\mathbb{H}$. These indices have very nice properties. First, they are invariant. under isometry and scaling. Further they can be, as in dimension $1$, easily estimated by using the so-called Pick and Freeze method. We investigate the asymptotic behaviour of such estimation scheme

Sensitivity indices for multivariate outputs

International audienceWe define and study a generalization of Sobol sensitivity indices for the case of a vector output.Nous définissons et étudions une généralisation des indices de Sobol pour des sorties vectorielles