Diskrete, mittelbare Gruppen.
Distances of Probability Measures and Uniform Distribution Mod 1.
Domain of partial attraction for infinitely divisible distributions in a Hilbert space
Domains of attraction in Banach spaces
Domains of attraction of stable measures on a Hilbert space
Domains of partial attraction in several dimensions
Doubly stochastic right multipliers.
Duality theorems for Kantorovich-Rubinstein and Wasserstein functionals [Book]
Dynamical semigroups and Markov processes on C*-algebras.
Dynamics of Stochastic Neuronal Networks and the Connections to Random Graph Theory
We analyze a stochastic neuronal network model which corresponds to an all-to-all network of discretized integrate-and-fire neurons where the synapses are failure-prone. This network exhibits different phases of behavior corresponding to synchrony and asynchrony, and we show that this is due to the limiting mean-field system possessing multiple attractors. We also show that this mean-field limit exhibits a first-order phase transition as a function...
EDSR, convergence en loi et homogénéisation d'EDP paraboliques semi-linéaires
Eigenvalues of random wreath products.
Eine Produktformel für Halbgruppen von Wahrscheinlichkeitsmaßen auf Lie-Gruppen.
Einige Bemerkungen zu den Gauss-Verteilungen auf lokalkompakten Abelschen Gruppen.
Elliptically contoured measures on infinite-dimensional Banach spaces
Embeddability of infinitely divisible distributions on linear Lie groups.
Embedding of an Infinitely Divisible Probality Measure over a Connected Solvable Lie Group.
Embedding of random vectors into continuous martingales
Let E be a real, separable Banach space and denote by the space of all E-valued random vectors defined on the probability space Ω. The following result is proved. There exists an extension of Ω, and a filtration on , such that for every there is an E-valued, continuous -martingale in which X is embedded in the sense that a.s. for an a.s. finite stopping time τ. For E = ℝ this gives a Skorokhod embedding for all , and for general E this leads to a representation of random vectors as...
Empirical estimates in stochastic optimization via distribution tails
“Classical” optimization problems depending on a probability measure belong mostly to nonlinear deterministic optimization problems that are, from the numerical point of view, relatively complicated. On the other hand, these problems fulfil very often assumptions giving a possibility to replace the “underlying” probability measure by an empirical one to obtain “good” empirical estimates of the optimal value and the optimal solution. Convergence rate of these estimates have been studied mostly for...
Ensembles analytiques, théorèmes de séparation et applications