The coding of compact real trees by real valued functions
Thomas Duquesne
Abstract
This paper is a detailled study of the coding of real trees by real valued functions that is motivated by probabilistic problems related to continuum random trees. Indeed it is known since the works of Aldous (1993) and Le Gall (1991) that a continuous non-negative function h on [0,1] such that h(0)=0 can be seen as the contour process of a compact real tree. This particular coding of a compact real tree provides additional structures, namely a root that is the vertex corresponding to 0∈ [0,1], a linear order inherited from the usual order on [0,1] and a measure induced by the Lebesgue measure on [0,1]; of course, the root, the linear order and the measure obtained by such a coding have to satisfy some compatibility conditions. In this paper, we prove that any compact real tree equipped with a root, a linear order and a measure that are compatible can be encoded by a non-negative function h defined on a finite interval [0, M], that is assumed to be left-continuous with right-limit, without positive jump and such that h(0+)=h(0)=0. Moreover, this function is unique if we assume that the exploration of the tree induced by such a coding backtracks as less as possible. We also prove that a measure-change on the tree corresponds to a re-parametrization of the coding function. In addition, we describe several path-properties of the coding function in terms of the metric properties of the real tree.
Create a lesson
Related papers
Almost sure exponential stability of multi-dimensional stochastic systems driven by a symmetric α-stable process
Xuekang Zhang, Hongjian Liu, Guangjun Shen
The Arc Threshold of the Two-Row Ising Torus: Exact Factorization, an Algebraic Critical Coupling, Uniformity in the Circumference, and the Rate of Convergence
Marco Mandap
Markovian Lifts and Multifactor Approximations for Weighted Sub-fractional Brownian Motion
J. H. Ramirez-Gonzalez, Edison Cuba
Rényi mixing bounds for kinetic samplers via L2 hypocoercivity
Jianfeng Lu, Yinchen Luo
Bayesian Filters via the Stochastic Category
Kenichi Bannai, Masahiro Kurisaki
Synchronization of Enriched Spin Glasses
Franziska Engler, Justin Ko