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Maximal brownian motions

Jean Brossard, Michel Émery, Christophe Leuridan (2009)

Annales de l'I.H.P. Probabilités et statistiques

Let Z=(X, Y) be a planar brownian motion, 𝒵 the filtration it generates, andBa linear brownian motion in the filtration 𝒵 . One says thatB(or its filtration) is maximal if no other linear 𝒵 -brownian motion has a filtration strictly bigger than that ofB. For instance, it is shown in [In Séminaire de Probabilités XLI 265–278 (2008) Springer] that B is maximal if there exists a linear brownian motion C independent of B and such that the planar brownian motion (B, C) generates the same filtration 𝒵 asZ....

Means in complete manifolds: uniqueness and approximation

Marc Arnaudon, Laurent Miclo (2014)

ESAIM: Probability and Statistics

Let M be a complete Riemannian manifold, M ∈ ℕ and p ≥ 1. We prove that almost everywhere on x = (x1,...,xN) ∈ MN for Lebesgue measure in MN, the measure μ ( x ) = N k = 1 N x k μ ( x ) = 1 N ∑ k = 1 N δ x k has a uniquep–mean ep(x). As a consequence, if X = (X1,...,XN) is a MN-valued random variable with absolutely continuous law, then almost surely μ(X(ω)) has a unique p–mean. In particular if (Xn)n ≥ 1 is an independent sample of an absolutely continuous law in M, then the process ep,n(ω) = ep(X1(ω),...,Xn(ω)) is...

Micro tangent sets of continuous functions

Zoltán Buczolich (2003)

Mathematica Bohemica

Motivated by the concept of tangent measures and by H. Fürstenberg’s definition of microsets of a compact set A we introduce micro tangent sets and central micro tangent sets of continuous functions. It turns out that the typical continuous function has a rich (universal) micro tangent set structure at many points. The Brownian motion, on the other hand, with probability one does not have graph like, or central graph like micro tangent sets at all. Finally we show that at almost all points Takagi’s...

Minkowski sums and Brownian exit times

Christer Borell (2007)

Annales de la faculté des sciences de Toulouse Mathématiques

If C is a domain in R n , the Brownian exit time of C is denoted by T C . Given domains C and D in R n this paper gives an upper bound of the distribution function of T C + D when the distribution functions of T C and T D are known. The bound is sharp if C and D are parallel affine half-spaces. The paper also exhibits an extension of the Ehrhard inequality

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