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A Relation between Brownian Bridge and Brownian Excursion

Wim Vervaat
- 01 Feb 1979 - 
- Vol. 7, Iss: 1, pp 143-149
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TLDR
In this article, it was shown that Brownian excursion is equal in distribution to Brownian bridge with the origin placed at its absolute minimum, which explains why the maximum of excursion and the range of brownian bridge have the same distribution, a fact which was discovered by Chung and Kennedy.
Abstract
It is shown that Brownian excursion is equal in distribution to Brownian bridge with the origin placed at its absolute minimum. This explains why the maximum of Brownian excursion and the range of Brownian bridge have the same distribution, a fact which was discovered by Chung and Kennedy. The result is proved by establishing similar relations for "Bernoulli excursions" and "Bernoulli bridges" constructed from symmetric Bernoulli walks, and exploiting known weak convergence results. Some technical complications arise from the fact that Bernoulli bridges assume their minimum value with positive probability more than once.

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Journal ArticleDOI

Convergence of Probability Measures

TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
BookDOI

Combinatorial Stochastic Processes

Jim Pitman
TL;DR: In this paper, the Brownian forest and the additive coalescent were constructed for random walks and random forests, respectively, and the Bessel process was used for random mappings.
Journal ArticleDOI

Probability laws related to the Jacobi theta and Riemann zeta functions, and Brownian excursions

TL;DR: In this paper, a review of known results which connect Riemann's integral representations of his zeta function, involving Jacobi's theta function and its derivatives, to some particular probability laws governing sums of independent exponential variables is presented.
Book

Essentials of Brownian Motion and Diffusion

TL;DR: In this paper, the authors present a survey of Markov processes with continuous paths, from the particular to the general, from a simple case to a probabilistic one, with a focus on non-ingular diffusion.
References
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Book

Convergence of Probability Measures

TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Journal ArticleDOI

Convergence of Probability Measures

TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Book

Brownian motion and diffusion

TL;DR: The book as discussed by the authors is part of a trilogy covering the field of Markov processes and provides a readable and constructive treatment of Brownian motion and diffusion, which dispenses with most of the customary transform apparatus.