On the Convergence of the Drainage Network with Branching

The Drainage Network is a system of coalescing random walks, exhibiting long-range dependence before coalescence, introduced by Gangopadhyay, Roy, and Sarkar [14]. Coletti, Fontes, and Dias [5] proved its convergence to the Brownian Web under diffusive scaling. In this work, we introduce a perturbation of the system allowing branching of the random walks with low probabilities varying with the scaling parameter. When the branching probability is inversely proportional to the scaling parameter, we show that this drainage network with branching consists of a tight family such that any weak limit point contains a Brownian Net. We conjecture that the limit is indeed the Brownian Net.

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Publication Details

Journal
Markov Processes And Related Fields
Published
2024-08-26
DOI
https://doi.org/10.61102/1024-2953-mprf.2024.30.2.001
Primary Topic
Stochastic processes and statistical mechanics
Type
article
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0.00
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article

On the Convergence of the Drainage Network with Branching

Leonel Zuaznábar, R. Santos, Glauco Valle
Markov Processes And Related Fields
Stochastic processes and statistical mechanics
article

On the Convergence of the Drainage Network with Branching

Leonel Zuaznábar, R. Santos, Glauco Valle
article en

Abstract

The Drainage Network is a system of coalescing random walks, exhibiting long-range dependence before coalescence, introduced by Gangopadhyay, Roy, and Sarkar [14]. Coletti, Fontes, and Dias [5] proved its convergence to the Brownian Web under diffusive scaling. In this work, we introduce a perturbation of the system allowing branching of the random walks with low probabilities varying with the scaling parameter. When the branching probability is inversely proportional to the scaling parameter, we show that this drainage network with branching consists of a tight family such that any weak limit point contains a Brownian Net. We conjecture that the limit is indeed the Brownian Net.

Markov Processes And Related Fields(2024 №2 (30))
Openalex Percentile: Top 75%
Stochastic processes and statistical mechanics
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