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We consider a model for chaotic diffusion with amplification on graphs associated with piecewise-linear maps of the interval. We investigate the possibility of having power-law tails in the invariant measure by approximate solution of the…

Chaotic Dynamics · Physics 2020-06-23 Stefano Lepri

Using the superstatistics method, we propose an extension of the random matrix theory to cover systems with mixed regular-chaotic dynamics. Unlike most of the other works in this direction, the ensembles of the proposed approach are basis…

Statistical Mechanics · Physics 2007-05-23 A. Y. Abul-Magd

We investigate stochastic averaging theory for locally Lipschitz discrete-time nonlinear systems with stochastic perturbation and its applications to convergence analysis of discrete-time stochastic extremum seeking algorithms. Firstly, by…

Optimization and Control · Mathematics 2015-02-18 Shu-Jun Liu , Miroslav Krstic

We investigate chaos in mixed-phase-space Hamiltonian systems using time series of the finite- time Lyapunov exponents. The methodology we propose uses the number of Lyapunov exponents close to zero to define regimes of ordered…

Chaotic Dynamics · Physics 2015-06-16 R. M. da Silva , C. Manchein , M. W. Beims , E. G. Altmann

Pairwise comparisons are widely used in decision analysis, preference modeling, and evaluation problems. In many practical situations, the observed comparison matrix is not reciprocal. This lack of reciprocity is often treated as a defect…

Machine Learning · Statistics 2026-04-07 Jean-Pierre Magnot

We study a notion of positivity of Gaussian directed acyclic graphical models corresponding to a non-negativity constraint on the coefficients of the associated structural equation model. We prove that this constraint is equivalent to the…

Statistics Theory · Mathematics 2023-06-01 Asad Lodhia , Jan-Christian Hütter , Caroline Uhler , Piotr Zwiernik

The $GARCH$ algorithm is the most renowned generalisation of Engle's original proposal for modelising {\it returns}, the $ARCH$ process. Both cases are characterised by presenting a time dependent and correlated variance or {\it…

Statistical Mechanics · Physics 2009-11-11 Silvio M. Duarte Queiros , Constantino Tsallis

Strong nonlinear effects combined with diffusive coupling may give rise to unpredictable evolution in spatially extended deterministic dynamical systems even in the presence of a fully negative spectrum of Lyapunov exponents. This regime,…

Chaotic Dynamics · Physics 2009-11-07 F. Ginelli , R. Livi , A. Politi

Chaotic features of systems of coupled Josephson junctions are studied. Manifestation of chaos in the temporal dependence of the electric charge, related to a parametric resonance, is demonstrated through the calculation of the maximal…

Superconductivity · Physics 2015-06-05 Yu. M. Shukrinov , M. Hamdipour , M. R. Kolahchi , A. E. Botha , M. Suzuki

We map the infinite-range coupled quantum kicked rotors over an infinite-range coupled interacting bosonic model. In this way we can apply exact diagonalization up to quite large system sizes and confirm that the system tends to ergodicity…

Quantum Physics · Physics 2021-06-09 Angelo Russomanno , Michele Fava , Rosario Fazio

Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth. Their main appeal is that they are exact, in the sense that…

Computation · Statistics 2015-03-25 Felipe J. Medina-Aguayo , Anthony Lee , Gareth O. Roberts

We study the statistics and scaling of extreme fluctuations in noisy task-completion landscapes, such as those emerging in synchronized distributed-computing networks, or generic causally-constrained queuing networks, with scale-free…

Disordered Systems and Neural Networks · Physics 2007-09-07 H. Guclu , G. Korniss , Z. Toroczkai

The framework of non-extensive statistical mechanics, proposed by Tsallis, has been used to describe a variety of systems. The non-extensive statistical mechanics is usually introduced in a formal way, thus simple models exhibiting some…

Statistical Mechanics · Physics 2014-08-06 Julius Ruseckas

Reliable prediction of large chaotic sytems in the short to middle time range is of interest in a number of fields, including climate, ecology, seismology, and economics. In this paper, results from chaos theory, and statistical theory are…

Applications · Statistics 2013-12-17 M. LuValle

Closed quantum systems evolve unitarily and therefore cannot converge in a strong sense to an equilibrium state starting out from a generic pure state. Nevertheless for large system size one observes temporal typicality. Namely, for the…

Quantum Physics · Physics 2013-03-21 Lorenzo Campos Venuti , Paolo Zanardi

We study the sum of first passage times along an arbitrary cycle made up of N>2 states of a small physical system. We show that, if the system is at thermodynamic equilibrium, this sum follows the same probability distribution regardless of…

Statistical Mechanics · Physics 2026-01-27 Daniel Maria Busiello , Shiling Liang , Simone Pigolotti

The paper deals with the theoretical analysis of a logistic system composed of at least two elements with distributed parameters. It has been shown that such a system may generate specific oscillations in spite of the fact that the…

Chaotic Dynamics · Physics 2026-02-10 Marek Berezowski , Artur Grabski

We present an efficient algorithm to compute tight upper bounds of collision probability between two objects with positional uncertainties, whose error distributions are represented with non-Gaussian forms. Our approach can handle noisy…

Robotics · Computer Science 2019-12-17 Jae Sung Park , Dinesh Manocha

The concept of "$A$-coupled-expanding" map for a transition matrix $A$ has been studied as one of the most important criteria of chaos in the past years. In this paper, the lower bound of the topological entropy for strictly…

Dynamical Systems · Mathematics 2013-10-01 Chol-Gyun Ri , Hyon-Hui Ju , Xiaoqun Wu

In complex and unknown processes, global models are initially generated over the entire experimental space but often fail to provide accurate predictions in local areas. A common approach is to use local models, which requires partitioning…

Machine Learning · Computer Science 2025-05-29 Dominik Polke , Tim Kösters , Elmar Ahle , Dirk Söffker