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Using a modification of the Shapiro approach, we introduce the two-parameter family of conductance distributions W(g), defined by simple differential equations, which are in the one-to-one correspondence with conductance distributions for…

Disordered Systems and Neural Networks · Physics 2017-08-02 I. M. Suslov

In the previous papers (Kui\'{c} et al. in Found Phys 42:319-339, 2012; Kui\'{c} in arXiv:1506.02622, 2015), it was demonstrated that applying the principle of maximum information entropy by maximizing the conditional information entropy,…

Statistical Mechanics · Physics 2016-06-14 Domagoj Kuic

For general absorbed Markov processes $(X_t)_{0\leq t<\tau_{\partial}}$ having a quasi-stationary distribution (QSD) $\pi$ and absorption time $\tau_{\partial}$, we introduce a Dobrushin-type criterion providing for exponential convergence…

Probability · Mathematics 2022-10-26 Oliver Tough

Local perturbations in conservative particle systems can have a non-local influence on the stationary measure. To capture this phenomenon, we analyze in this paper two toy models. We study the symmetric exclusion process on a countable set…

Probability · Mathematics 2024-10-25 Frank Redig , Ellen Saada

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in…

Statistical Mechanics · Physics 2024-09-26 Julius Degünther , Jann van der Meer , Udo Seifert

This paper discusses semiparametric inference on hypotheses on the cointegration and the attractor spaces for $I(1)$ linear processes with moderately large cross-sectional dimension. The approach is based on empirical canonical correlations…

Methodology · Statistics 2025-12-17 Massimo Franchi , Paolo Paruolo

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory,…

Machine Learning · Statistics 2025-03-06 Salomé A. Sepúveda Fontaine , José M. Amigó

Strong invariance principles describe the error term of a Brownian approximation of the partial sums of a stochastic process. While these strong approximation results have many applications, the results for continuous-time settings have…

Statistics Theory · Mathematics 2022-06-17 Ardjen Pengel , Joris Bierkens

We study the properties of the random quantum states induced from the uniformly random pure states on a bipartite quantum system by taking the partial trace over the larger subsystem. Most of the previous studies have adopted a viewpoint of…

Quantum Physics · Physics 2023-11-29 Eyuri Wakakuwa

It has recently been shown that there are substantial differences in the regularity behavior of the empirical process based on scalar diffusions as compared to the classical empirical process, due to the existence of diffusion local time.…

Probability · Mathematics 2011-05-25 Angelika Rohde , Claudia Strauch

For probability measures on countable spaces we derive distributional limits for empirical entropic optimal transport quantities. More precisely, we show that the empirical optimal transport plan weakly converges to a centered Gaussian…

Probability · Mathematics 2022-12-27 Shayan Hundrieser , Marcel Klatt , Axel Munk

We extend to Lipschitz continuous functionals either of the true paths or of the Euler scheme with decreasing step of a wide class of Brownian ergodic diffusions, the Central Limit Theorems formally established for their marginal empirical…

Probability · Mathematics 2013-04-03 Gilles Pagès , Fabien Panloup

The entropy production is one of the most essential features for systems operating out of equilibrium. The formulation for discrete-state systems goes back to the celebrated Schnakenberg's work and hitherto can be carried out when for each…

Statistical Mechanics · Physics 2020-04-15 D. M. Busiello , D. Gupta , A. Maritan

We develop a likelihood-based inference for finite-state birth-death processes with composite birth rates, in which multiple distinct mechanisms contribute additively to the total birth intensity. Our main motivating example is an SIS…

Statistics Theory · Mathematics 2026-04-23 Marko Lalovic , Nicos Georgiou , Istvan Z. Kiss

There is a relation between the irreversibility of thermodynamic processes as expressed by the breaking of time-reversal symmetry, and the entropy production in such processes. We explain on an elementary mathematical level the relations…

Statistical Mechanics · Physics 2007-05-23 C. Maes , K. Netocny

We develop a quenched thermodynamic formalism for open random dynamical systems generated by finitely branched, piecewise-monotone mappings of the interval. The openness refers to the presence of holes in the interval, which terminate…

Dynamical Systems · Mathematics 2024-11-06 Jason Atnip , Gary Froyland , Cecilia Gonzalez-Tokman , Sandro Vaienti

The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has been very popular in machine learning due to its "Occam's…

Machine Learning · Computer Science 2016-07-13 Yuanzhi Li , Andrej Risteski

A piecewise-deterministic Markov process, specified by random jumps and switching semi-flows, as well as the associated Markov chain given by its post-jump locations, are investigated in this paper. The existence of an exponentially…

Probability · Mathematics 2020-12-07 Dawid Czapla , Katarzyna Horbacz , Hanna Wojewódka-Ściążko

The weak law of large numbers implies that, under mild assumptions on the source, the Renyi entropy per produced symbol converges (in probability) towards the Shannon entropy rate. This paper quantifies the speed of this convergence for…

Information Theory · Computer Science 2017-05-01 Maciej Skorski

Latent dynamical models are commonly used to learn the distribution of a latent dynamical process that represents a sequence of noisy data samples. However, producing samples from such models with high fidelity is challenging due to the…

Machine Learning · Computer Science 2023-08-17 Mohammad R. Rezaei