On exponentially height-penalized random trees
Louigi Addario-Berry, Beno\^it Corsini, Neeladri Maitra, Meltem \"Unel

TL;DR
This paper investigates the asymptotic behavior of height-biased random trees with exponential height penalties, extending previous fixed-parameter results to variable parameters depending on the size, revealing phase transitions and new structural statistics.
Contribution
It generalizes the asymptotic analysis of height-biased trees to arbitrary sequences of parameters, uncovering phase transitions and detailed structural properties.
Findings
Tree height behaves like a height-biased CRT when $rac{1}{\sqrt{n}}$
Height scales as $(2\pi^2 n/\mu_n)^{1/3}$ for larger $rac{1}{\sqrt{n}}$
Height converges to a constant for $rac{1}{n}$ or larger regimes
Abstract
Given and , a \textit{\mun} is a random plane tree with vertices with law given by , where ranges over fixed plane trees with vertices, and is the height of . Fix a sequence of real numbers, and for let be a -height-biased tree of size . Durhuus and \"Unel (2023) described the asymptotic behaviour of when is fixed. In this work, we extend their results to arbitrary sequences of positive parameters depending on . Most notably, we show that such a tree behaves like a height-biased Continuum Random Tree (CRT) when is of order ; that its height is asymptotically when is…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Theoretical and Computational Physics · Financial Risk and Volatility Modeling
