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In this article, we address the decay of correlations for dynamical systems that admit an induced weak Gibbs Markov map (not necessarily full branch). Our approach generalizes L.-S. Young's coupling arguments to estimate the decay of…

Dynamical Systems · Mathematics 2023-11-30 Asad Ullah , Helder Vilarinho

We develop a quenched thermodynamic formalism for random dynamical systems generated by countably branched, piecewise-monotone mappings of the interval that satisfy a random covering condition. Given a random contracting potential $\varphi$…

Dynamical Systems · Mathematics 2021-07-16 Jason Atnip , Gary Froyland , Cecilia González-Tokman , Sandro Vaienti

We consider a random walk in a random potential on a square lattice of arbitrary dimension. The potential is a function of an ergodic environment and some steps of the walk. The potential can be unbounded, but it is subject to a moment…

Probability · Mathematics 2013-02-12 Firas Rassoul-Agha , Timo Seppäläinen

We prove a multidimensional ergodic theorem with weighted averages for the action of the group $\mathbb{Z}^d$ on a probability space. At level $n$ weights are of the form $n^{-d} \psi(j/n)$, $ j\in \mathbb{Z}^d$, for real functions $\psi$…

Probability · Mathematics 2024-11-19 A. Faggionato

For smooth random dynamical systems we consider the quenched linear and higher-order response of equivariant physical measures to perturbations of the random dynamics. We show that the spectral perturbation theory of Gou\"ezel, Keller, and…

Dynamical Systems · Mathematics 2021-05-25 Harry Crimmins , Yushi Nakano

We prove a vector-valued almost sure invariance principle for some classes of time dependent non-uniformly distance expanding dynamical systems. The models we have in mind are certain sequential versions of the smooth non-uniformly distance…

Dynamical Systems · Mathematics 2020-05-14 Yeor Hafouta

We consider a random walk in random environment with random holding times, that is, the random walk jumping to one of its nearest neighbors with some transition probability after a random holding time. Both the transition probabilities and…

Probability · Mathematics 2014-12-30 Ryoki Fukushima , Naoki Kubota

Non-convex sampling is a key challenge in machine learning, central to non-convex optimization in deep learning as well as to approximate probabilistic inference. Despite its significance, theoretically there remain many important…

Machine Learning · Computer Science 2024-09-18 Mohammad Reza Karimi , Ya-Ping Hsieh , Andreas Krause

Random matrix theory (RMT) successfully predicts universal statistical properties of complicated wave scattering systems in the semiclassical limit, while the random coupling model offers a complete statistical model with a simple additive…

In this paper, we study a Galton-Watson process $(Z_n)$ with infinitely many types in a random ergodic environment $\bar{\xi}=(\xi_n)_{n\geq 0}$. We focus on the supercritical regime of the process, where the quenched average of the size of…

Probability · Mathematics 2025-02-07 Maxime Ligonnière

In this dissertation, we show that the Central Limit Theorem and the Invariance Principle for Discrete Fourier Transforms discovered by Peligrad and Wu can be extended to the quenched setting. We show that the random normalization…

Probability · Mathematics 2016-05-25 David Barrera

We prove a quenched invariance principle for a class of random walks in random environment on $\mathbb{Z}^d$, where the walker alters its own environment. The environment consists of an outgoing edge from each vertex. The walker updates the…

Probability · Mathematics 2021-07-02 Swee Hong Chan , Lila Greco , Lionel Levine , Peter Li

The convergence rate in Wasserstein distance is estimated for empirical measures of ergodic Markov processes, and the estimate can be sharp in some specific situations. The main result is applied to subordinations of typical models excluded…

Probability · Mathematics 2024-08-14 Feng-Yu Wang

We study an intermittent quasistatic dynamical system composed of nonuniformly hyperbolic Pomeau--Manneville maps with time-dependent parameters. We prove an ergodic theorem which shows almost sure convergence of time averages in a certain…

Dynamical Systems · Mathematics 2016-06-22 Juho Leppänen , Mikko Stenlund

We prove an almost sure invariance principle for a random walker among i.i.d. conductances in $\Z^d$, $d\geq 2$. We assume conductances are bounded from above but we dot require they are bounded from below.

Probability · Mathematics 2012-09-11 P. Mathieu

We study jump-diffusion processes with parameters switching at random times. Being motivated by possible applications, we characterise equivalent martingale measures for these processes by means of the relative entropy. The minimal entropy…

Probability · Mathematics 2015-08-21 Antonio Di Crescenzo , Nikita Ratanov

Consider a random walk on $\mathbb{Z}^d$ in a translation-invariant and ergodic random environment and starting from the origin. In this short note, assuming that a quenched invariance principle for the opportunely-rescaled walks holds, we…

Probability · Mathematics 2025-12-09 Alberto Chiarini , Simone Floreani , Federico Sau

We prove quenched versions of a central limit theorem, a large deviations principle as well as a local central limit theorem for expanding on average cocycles. This is achieved by building an appropriate modification of the spectral method…

Dynamical Systems · Mathematics 2021-11-25 Davor Dragičević , Julien Sedro

We consider independently identically distributed random compositions of the Gauss and R\'enyi maps that generate random continued fractions. Using methods of ergodic theory, thermodynamic formalism and large deviations, we show that…

Dynamical Systems · Mathematics 2025-10-29 Shintaro Suzuki , Hiroki Takahasi

We study random walks on $\mathbb Z^d$ (with $d\ge 2$) among stationary ergodic random conductances $\{C_{x,y}\colon x,y\in\mathbb Z^d\}$ that permit jumps of arbitrary length. Our focus is on the Quenched Invariance Principle (QIP) which…

Probability · Mathematics 2023-10-05 Marek Biskup , Xin Chen , Takashi Kumagai , Jian Wang