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Reflecting Brownian motion can be constructed as a strong Markov process not only on a domain with smooth or Lipschitz boundary with H¨ older cusps (see Lions and Sznitman [10],
(2007) use ingenious arguments based on ideas from differential games to show that a bounded convex planar domain cannot support any shy couplings of reflected Brownian motions if
By Skorokhod embedding of mean zero, finite variance random variables in Brownian motion, there is a stopping time τ such that for any standard Brownian motion (i.e., starting
Brownian motion, fine topology, local maxima, optional
In the paper [STW], it was shown that “a Brownian motion reflected on an independent time-reversed Brownian motion is again a Brownian motion” combining the fact that the
Our main subjects in the present paper are divergence formulae for two types of Wiener spaces consisting of a Brownian motion starting from zero and a pinned Brownian motion
Brownian motion, loop-erased random walk, Green’s function esti- mates, excursion Poisson kernel, Fomin’s identity, strong approximation.. Submitted to EJP on March
A proof of the increase in maturity of the expectation of a convex function of the arithmetic average of geometric brownian motion. Making Markov martingales meet marginals:
