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The dissipation of general convex entropies for continuous time Markov processes can be described in terms of backward martingales with respect to the tail filtration. The relative entropy is the expected value of a backward submartingale.…

Probability · Mathematics 2015-01-27 Joaquin Fontbona , Benjamin Jourdain

We formulate a trajectorial version of the relative entropy dissipation identity for McKean$-$Vlasov diffusions, extending the results of the papers [FJ16,KST20a], which apply to non-interacting diffusions. Our stochastic analysis approach…

Probability · Mathematics 2021-05-27 Bertram Tschiderer , Lane Chun Yeung

We revisit the variational characterization of diffusion as entropic gradient flux and provide for it a probabilistic interpretation based on stochastic calculus. It was shown by Jordan, Kinderlehrer, and Otto that, for diffusions of…

Probability · Mathematics 2020-03-24 Ioannis Karatzas , Walter Schachermayer , Bertram Tschiderer

The dissipation phenomena of relative entropy from an It\^o--Langevin dynamical system is a classic topic from stochastic analysis. Relying on the time-reversal of diffusions, a novel trajectorial approach investigates the pathwise behavior…

Probability · Mathematics 2025-10-03 Jiaming Chen

We revisit the variational characterization of conservative diffusion as entropic gradient flow and provide for it a probabilistic interpretation based on stochastic calculus. It was shown by Jordan, Kinderlehrer, and Otto that, for…

Probability · Mathematics 2020-08-24 Ioannis Karatzas , Walter Schachermayer , Bertram Tschiderer

We study the exponential dissipation of entropic functionals for continuous time Markov chains and the associated convex Sobolev inequalities, including MLSI and Beckner inequalities. We propose a method that combines the Bakry \'Emery…

Probability · Mathematics 2020-05-28 Giovanni Conforti

In his 2011 work, Maas has shown that the law of any time-reversible continuous-time Markov chain with finite state space evolves like a gradient flow of the relative entropy with respect to its stationary distribution. In this work we show…

Probability · Mathematics 2015-04-03 Helge Dietert

Let K be an irreducible and reversible Markov kernel on a finite set X. We construct a metric W on the set of probability measures on X and show that with respect to this metric, the law of the continuous time Markov chain evolves as the…

Probability · Mathematics 2011-06-17 Jan Maas

We study the time evolution of velocity and pressure gradients in isotropic turbulence, by quantifying their decorrelation time scales as one follows fluid particles in the flow. The Lagrangian analysis uses data in a public database…

Fluid Dynamics · Physics 2015-05-14 Huidan Yu , Charles Meneveau

We study a new notion of Ricci curvature that applies to Markov chains on discrete spaces. This notion relies on geodesic convexity of the entropy and is analogous to the one introduced by Lott, Sturm, and Villani for geodesic measure…

Metric Geometry · Mathematics 2015-06-03 Matthias Erbar , Jan Maas

A classical approach for the analysis of the longtime behavior of Markov processes is to consider suitable Lyapunov functionals like the variance or more generally $\Phi$-entropies. Via purely analytic arguments it can be shown that these…

Probability · Mathematics 2023-07-26 Benedikt Jahnel , Jonas Köppl

Tracers in a turbulent flow separate according to the celebrated $t^{3/2}$ Richardson--Obukhov law, which is usually explained by a scale-dependent effective diffusivity. Here, supported by state-of-the-art numerics, we revisit this…

Fluid Dynamics · Physics 2015-06-05 Rehab Bitane , Jérémie Bec , Holger Homann

In this paper we investigate the application of non-local graph entropy to evolving and dynamical graphs. The measure is based upon the notion of Markov diffusion on a graph, and relies on the entropy applied to trajectories originating at…

Physics and Society · Physics 2016-06-27 Francesco Caravelli

The relative entropy and chi-squared divergence are fundamental divergence measures in information theory and statistics. This paper is focused on a study of integral relations between the two divergences, the implications of these…

Information Theory · Computer Science 2020-06-24 Tomohiro Nishiyama , Igal Sason

We analyse diffusion dynamics on weakly-coupled networks (interconnected networks) by means of separation of time scales. Using an adiabatic approximation we reduced the system dynamics to a Markov chain with aggregated variables and…

Chaotic Dynamics · Physics 2018-12-14 Grzegorz Siudem , Janusz A. Hołyst

While entropy changes are the usual subject of fluctuation theorems, we seek fluctuation relations involving time-symmetric quantities, namely observables that do not change sign if the trajectories are observed backward in time. We find…

Statistical Mechanics · Physics 2015-11-04 Marco Baiesi , Gianmaria Falasco

Convexity properties of the entropy along displacement interpolations are crucial in the Lott-Sturm-Villani theory of lower bounded curvature of geodesic measure spaces. As discrete spaces fail to be geodesic, an alternate analogous theory…

Probability · Mathematics 2022-09-05 Christian Léonard

We consider the problem of estimating the joint distribution of a continuous-time perpetuity and the underlying factors which govern the cash flow rate, in an ergodic Markov model. Two approaches are used to obtain the distribution. The…

Probability · Mathematics 2016-01-18 Constantinos Kardaras , Scott Robertson

Motivated by entropic optimal transport, time reversal of diffusion processes is revisited. An integration by parts formula is derived for the carr\'e du champ of a Markov process in an abstract space. It leads to a time reversal formula…

Probability · Mathematics 2022-09-05 Patrick Cattiaux , Giovanni Conforti , Ivan Gentil , Christian Léonard

We consider degenerate diffusion equations of the form $\partial_tp_t = \Delta f(p_t)$ on a bounded domain and subject to no-flux boundary conditions, for a class of nonlinearities $f$ that includes the porous medium equation. We derive for…

Probability · Mathematics 2022-10-31 Donghan Kim , Lane Chun Yeung
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