Resumen de Scaling limits of Markov branching trees with applications to Galton–Watson and random unordered trees
Bénédicte Haas, Grégory Miermont
We consider a family of random trees satisfying a Markov branching property. Roughly, this property says that the subtrees above some given height are independent with a law that depends only on their total size, the latter being either the number of leaves or vertices. Such families are parameterized by sequences of distributions on partitions of the integers that determine how the size of a tree is distributed in its different subtrees. Under some natural assumption on these distributions, stipulating that “macroscopic” splitting events are rare, we show that Markov branching trees admit the so-called self-similar fragmentation trees as scaling limits in the Gromov–Hausdorff–Prokhorov topology.
The main application of these results is that the scaling limit of random uniform unordered trees is the Brownian continuum random tree. This extends a result by Marckert–Miermont and fully proves a conjecture by Aldous. We also recover, and occasionally extend, results on scaling limits of consistent Markov branching models and known convergence results of Galton–Watson trees toward the Brownian and stable continuum random trees.
