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Scaling Limit of a Stochastic Clustering Model on ℝ

2025/11/19 by Dey, Partha S., Etesami, S. Rasoul, Gopalan, Aditya S.
#FOS: Mathematics #Probability (math.PR)

paper · doi:10.48550/arxiv.2511.15089

Abstract

We consider an infinite-dimensional stochastic clustering model on ℝ. In discrete time, each point of a unit-intensity simple point process moves halfway toward either of its left or right neighbors, chosen uniformly at random. Co-located points are merged into a single point, and the resulting simple point process is rescaled to unit intensity. We show that, when the point processes are shifted so that there is a point at the origin, there is a unique weak limit of these dynamics when the initial point process is renewal. We also conjecture that for the time-reversed process and with an appropriate scaling in space, there is a limiting (random) distribution function on ℝ, whose corresponding measure assigns to ℝ a measure corresponding to the gap between consecutive points. Finally, we discuss some relevant research directions.

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