Let B1, B2,... be independent one-dimensional Brownian motions defined over the whole real line such that Bi(0) = 0 for every i ≥ 1. We consider the nth iterated Brownian motion Wn(t) = Bn(Bn−1(...(B2(B1(t)))...)). Although the sequences of processes (Wn)n≥1 do not converge in a functional sense, we prove that the finite-dimensional marginals converge. As a consequence, we deduce that the random occupation measures of Wn converge towards a random probability measure µ∞. We then prove that µ ∞ almost surely has a continu-ous density which must be thought of as the local time process of the infinite iteration of independent Brownian motions
AbstractIn this article, we study the family of probability measures (indexed by t∈R+∗), obtained by...
Algorithmic randomness is most often studied in the setting of the fair-coin measure on the Cantor s...
A family of measures, on the set of partitions of an integer, known as the Ewens sampling formula ar...
Abstract Let B1,B2,...be independent one-dimensional Brownian motions parameterized by the whole rea...
To motivate of why it could be interesting to study multidimensional Brownian motion conditioned to ...
AbstractLet τD(Z) be the first exit time of iterated Brownian motion from a domain D⊂Rn started at z...
We consider iid Brownian motions, Bj(t), where Bj(0) has a rapidly decreasing, smooth density functi...
We consider lid Brownian motions, B(j)(t), where B(j)(0) has a rapidly decreasing, smooth density fu...
AbstractLet X1,X2,… be i.i.d. random variables with a continuous distribution function. Let R0=0, Rk...
Let [tau]D(Z) be the first exit time of iterated Brownian motion from a domain started at z[set memb...
We present a constructive probabilistic proof of the fact that if B = (Bt)t≥0 is standard Brownian m...
[[abstract]]Let (Xn)n≥1 be a sequence of random variables with zero means and uniformly bounded vari...
In Chapter 1, iterated Brownian motion started at [special characters omitted] is defined by [specia...
Motivated by the central limit theorem for weakly dependent variables, we show that the Brownian mot...
AbstractA class of iterated processes is studied by proving a joint functional limit theorem for a p...
AbstractIn this article, we study the family of probability measures (indexed by t∈R+∗), obtained by...
Algorithmic randomness is most often studied in the setting of the fair-coin measure on the Cantor s...
A family of measures, on the set of partitions of an integer, known as the Ewens sampling formula ar...
Abstract Let B1,B2,...be independent one-dimensional Brownian motions parameterized by the whole rea...
To motivate of why it could be interesting to study multidimensional Brownian motion conditioned to ...
AbstractLet τD(Z) be the first exit time of iterated Brownian motion from a domain D⊂Rn started at z...
We consider iid Brownian motions, Bj(t), where Bj(0) has a rapidly decreasing, smooth density functi...
We consider lid Brownian motions, B(j)(t), where B(j)(0) has a rapidly decreasing, smooth density fu...
AbstractLet X1,X2,… be i.i.d. random variables with a continuous distribution function. Let R0=0, Rk...
Let [tau]D(Z) be the first exit time of iterated Brownian motion from a domain started at z[set memb...
We present a constructive probabilistic proof of the fact that if B = (Bt)t≥0 is standard Brownian m...
[[abstract]]Let (Xn)n≥1 be a sequence of random variables with zero means and uniformly bounded vari...
In Chapter 1, iterated Brownian motion started at [special characters omitted] is defined by [specia...
Motivated by the central limit theorem for weakly dependent variables, we show that the Brownian mot...
AbstractA class of iterated processes is studied by proving a joint functional limit theorem for a p...
AbstractIn this article, we study the family of probability measures (indexed by t∈R+∗), obtained by...
Algorithmic randomness is most often studied in the setting of the fair-coin measure on the Cantor s...
A family of measures, on the set of partitions of an integer, known as the Ewens sampling formula ar...