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P\'olya trees are rooted trees considered up to symmetry. We establish the convergence of large uniform random P\'olya trees with arbitrary degree restrictions to Aldous' Continuum Random Tree with respect to the Gromov-Hausdorff metric.…

Probability · Mathematics 2016-12-12 Konstantinos Panagiotou , Benedikt Stufler

We study time-inhomogeneous random walks on finite groups in the case where each random walk step need not be supported on a generating set of the group. When the supports of the random walk steps satisfy a natural condition involving…

Probability · Mathematics 2026-02-04 Elia Gorokhovsky

In this work, we are interested in the set of visited vertices of a tree $\mathbb{T}$ by a randomly biased random walk $\mathbb{X}:=(X_n,n \in \mathbb{N})$. The aim is to study a generalized range, that is to say the volume of the trace of…

Probability · Mathematics 2024-09-20 Pierre Andreoletti , Alexis Kagan

In the context of countable groups of polynomial volume growth, we consider a large class of random walks that are allowed to take long jumps along multiple subgroups according to power law distributions. For such a random walk, we study…

Probability · Mathematics 2022-07-26 Zhen-Qing Chen , Takashi Kumagai , Laurent Saloff-Coste , Jian Wang , Tianyi Zheng

We introduce a broad class of random graph models: the generalised hypergeometric ensemble (GHypEG). This class enables to solve some long standing problems in random graph theory. First, GHypEG provides an elegant and compact formulation…

Probability · Mathematics 2021-07-06 Giona Casiraghi , Vahan Nanumyan

We consider a random walk on a homogeneous space $G/\Lambda$ where $G$ is a non-compact simple Lie group and $\Lambda$ is a lattice. The walk is driven by a probability measure $\mu$ on $G$ whose support generates a Zariski-dense subgroup.…

Dynamical Systems · Mathematics 2026-05-27 Timothée Bénard , Weikun He

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not scale well due to the quadratic complexity. In this paper, a…

Machine Learning · Computer Science 2012-10-19 Saeed Amizadeh , Bo Thiesson , Milos Hauskrecht

Normalised generalised gamma processes are random probability measures that induce nonparametric prior distributions widely used in Bayesian statistics, particularly for mixture modelling. We construct a class of dependent normalised…

Probability · Mathematics 2016-11-07 Matteo Ruggiero , Matteo Sordello

We study the fundamental question of how likely it is that two randomly chosen trees are isomorphic to each other for different models of random trees. We show that the probability decays exponentially for rooted labeled trees as well as…

Probability · Mathematics 2023-04-11 Christoffer Olsson

Disordered systems such as spin glasses have been used extensively as models for high-dimensional random landscapes and studied from the perspective of optimization algorithms. In a recent paper by L. Addario-Berry and the second author,…

Probability · Mathematics 2022-06-17 Fu-Hsuan Ho , Pascal Maillard

We collect, survey and develop methods of (one-dimensional) stochastic approximation in a framework that seems suitable to handle fairly broad generalizations of Polya urns. To show the applicability of the results we determine the limiting…

Probability · Mathematics 2010-02-22 Henrik Renlund

Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gradient descent methodology, a class of algorithms that rely…

Machine Learning · Statistics 2023-02-14 A. Duncan , N. Nuesken , L. Szpruch

We present a rather general method for proving local limit theorems, with a good rate of convergence, for sums of dependent random variables. The method is applicable when a Stein coupling can be exhibited. Our approach involves both…

Probability · Mathematics 2020-07-07 A. D. Barbour , Peter Braunsteins , Nathan Ross

In \cite{n-p-noncentral}, Nourdin and Peccati established a neat characterization of Gamma approximation on a fixed Wiener chaos in terms of convergence of only the third and fourth cumulants. In this paper, we provide an optimal rate of…

Probability · Mathematics 2019-02-08 Ehsan Azmoodeh , Peter Eichelsbacher , Lukas Knichel

Multivariate generalized Gamma convolutions are distributions defined by a convolutional semi-parametric structure. Their flexible dependence structures, the marginal possibilities and their useful convolutional expression make them…

Statistics Theory · Mathematics 2022-03-28 Oskar Laverny

This article is concerned with numerical methods to approximate effective coefficients in stochastic homogenization of discrete linear elliptic equations, and their numerical analysis --- which has been made possible by recent contributions…

Numerical Analysis · Mathematics 2012-11-09 A. -C. Egloffe , A. Gloria , J. -C. Mourrat , T. N. Nguyen

Sinai's model of diffusion in one-dimension with random local bias is studied by a real space renormalization group which yields asymptotically exact long time results. The distribution of the position of a particle and the probability of…

Condensed Matter · Physics 2009-10-30 Daniel Fisher , Pierre Le Doussal , Cecile Monthus

We introduce the P\'olya threshold graph model and derive its stochastic and algebraic properties. This random threshold graph is generated sequentially via a two-color P\'olya urn process. Starting from an empty graph, each time step…

Information Theory · Computer Science 2026-03-20 Jinghan Yu , Fady Alajaji , Bahman Gharesifard

In [NP09a], Nourdin and Peccati established a neat characterization of Gamma approximation on a fixed Wiener chaos in terms of convergence of only the third and fourth cumulants. In this paper, we investigate the rate of convergence in…

Probability · Mathematics 2018-10-24 Ehsan Azmoodeh , Peter Eichelsbacher , Lukas Knichel

In this paper, we show that a Galton-Watson tree conditioned to have a fixed number of particles in generation $n$ converges in distribution as $n\rightarrow\infty$, and with this tool we study the span and gap statistics of a branching…

Probability · Mathematics 2021-11-24 Tianyi Bai , Pierre Rousselin
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