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Filtering is concerned with the sequential estimation of the state, and uncertainties, of a Markovian system, given noisy observations. It is particularly difficult to achieve accurate filtering in complex dynamical systems, such as those…

Probability · Mathematics 2015-12-14 Wonjung Lee , Andrew Stuart

We study the connection between damage spreading, a phenomenon long discussed in the physics literature, and the coupling of Markov chains, a technique used to bound the mixing time. We discuss in parallel the Edwards-Anderson spin glass…

Disordered Systems and Neural Networks · Physics 2025-02-26 Koji Hukushima , Werner Krauth

We develop a microscopic theory of thermalisation for a thermometer coupled to a many-body bath beyond standard Markovian and Fermi-golden-rule assumptions. By modeling interaction matrix elements in the non-interacting basis as independent…

Disordered Systems and Neural Networks · Physics 2026-03-13 Aleksey Lunkin

We study Lorentz processes in two different settings. Both cases are characterized by infinite expectation of the free-flight times, contrary to what happens in the classical Gallavotti-Spohn models. Under a suitable Boltzmann-Grad type…

Probability · Mathematics 2025-09-23 Lorenzo Facciaroni , Costantino Ricciuti , Enrico Scalas , Bruno Toaldo

We consider the time-optimal problem for a classical system of "double integrator" under the presence of a linear state constraint. By using the Maximum Principle of Dubovitskii and Milyutin, we determine a complete synthesis of optimal…

Optimization and Control · Mathematics 2022-01-31 Andrei Dmitruk , Ivan Samylovskiy

We address the problem of estimating the mixing time of a Markov chain from a single trajectory of observations. Unlike most previous works which employed Hilbert space methods to estimate spectral gaps, we opt for an approach based on…

Probability · Mathematics 2023-09-13 Geoffrey Wolfer

In this paper we present a novel method for estimating the parameters of a parametric diffusion processes. Our approach is based on a closed-form Maximum Likelihood estimator for an approximating Continuous Time Markov Chain (CTMC) of the…

Methodology · Statistics 2021-08-31 J. L. Kirkby , Dang Nguyen , Duy Nguyen , Nhu Nguyen

We study Brownian flows on manifolds for which the associated Markov process is strongly mixing with respect to an invariant probability measure and for which the distance process for each pair of trajectories is a diffusion $r$. We provide…

Probability · Mathematics 2015-11-02 Michael Cranston , Benjamin Gess , Michael Scheutzow

The problem of constructing an optimal co-adapted coupling for a pair of symmetric random walks on $Z_2^d$ was considered by Connor and Jacka (2008), and the existence of a coupling which is stochastically fastest in the class of all such…

Probability · Mathematics 2014-03-03 Stephen B. Connor

Recently a new theory for the transport of energetic particles across a mean magnetic field was presented. Compared to other non-linear theories the new approach has the advantage that it provides a full time-dependent description of the…

Plasma Physics · Physics 2017-09-20 Jordan Lasuik , Andreas Shalchi

We derive P(M,t_m), the joint probability density of the maximum M and the time t_m at which this maximum is achieved for a class of constrained Brownian motions. In particular, we provide explicit results for excursions, meanders and…

Statistical Mechanics · Physics 2008-10-31 Satya. N. Majumdar , Julien Randon-Furling , Michael J. Kearney , Marc Yor

This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multiple access and broadcast systems. Every time slot an access point observes the current channel states for…

Optimization and Control · Mathematics 2020-03-18 Michael J. Neely

In this article integro-differential Volterra equations whose convolution kernel depends on the vector variable are considered and a connection of these equations with a class of semi-Markov processes is established. The variable order…

Probability · Mathematics 2018-07-19 Mladen Savov , Bruno Toaldo

We consider an array of units each of which can be in one of three states. Unidirectional transitions between these states are governed by Markovian rate processes.The interactions between units occur through a dependence of the transition…

Statistical Mechanics · Physics 2015-06-22 Daniel Escaff , Italo'Ivo Lima Dias Pinto , Katja Lindenberg

An efficient optimal-control theory based on the Krotov method is introduced for a non-Markovian open quantum system with a time-nonlocal master equation in which the control parameter and the bath correlation function are correlated. This…

Quantum Physics · Physics 2015-06-04 Bin Hwang , Hsi-Sheng Goan

Inhomogeneous phase-type (IPH) distributions extend classical phase-type models by allowing transition intensities to vary over time, offering greater flexibility for modeling heavy-tailed or time-dependent absorption phenomena. We focus on…

Methodology · Statistics 2025-12-19 Fernando Baltazar-Larios , Alejandra Quintos

We discuss the situations under which Brownian yet non-Gaussian (BnG) diffusion can be observed in the model of a particle's motion in a random landscape of diffusion coefficients slowly varying in space. Our conclusion is that such…

Statistical Mechanics · Physics 2020-01-15 E. B. Postnikov , A. Chechkin , I. M. Sokolov

Stimulated by experimental progress in high energy physics and astrophysics, the unification of relativistic and stochastic concepts has re-attracted considerable interest during the past decade. Focusing on the framework of special…

Statistical Mechanics · Physics 2009-02-13 Jörn Dunkel , Peter Hänggi

This paper considers the problem of randomized influence maximization over a Markovian graph process: given a fixed set of nodes whose connectivity graph is evolving as a Markov chain, estimate the probability distribution (over this fixed…

Social and Information Networks · Computer Science 2017-11-10 Buddhika Nettasinghe , Vikram Krishnamurthy

We provide a coupling proof of Doob's theorem which says that the transition probabilities of a regular Markov process which has an invariant probability measure $\mu$ converge to $\mu$ in the total variation distance. In addition we show…

Probability · Mathematics 2014-08-01 Alexei Kulik , Michael Scheutzow