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We study the convergence of divergence-regularized optimal transport as the regularization parameter vanishes. Sharp rates for general divergences including relative entropy or $L^{p}$ regularization, general transport costs and…

Optimization and Control · Mathematics 2023-06-22 Stephan Eckstein , Marcel Nutz

We investigate a quadratic dynamical system known as nonlinear recombinations. This system models the evolution of a probability measure over the Boolean cube, converging to the stationary state obtained as the product of the initial…

Probability · Mathematics 2024-10-07 Pietro Caputo , Cyril Labbé , Hubert Lacoin

The Brunn-Minkowski inequality, applicable to bounded measurable sets $A$ and $B$ in $\mathbb{R}^d$, states that $|A+B|^{1/d} \geq |A|^{1/d}+|B|^{1/d}$. Equality is achieved if and only if $A$ and $B$ are convex and homothetic sets in…

Analysis of PDEs · Mathematics 2024-07-16 Alessio Figalli , Peter van Hintum , Marius Tiba

We study the strong approximation of a rough volatility model, in which the log-volatility is given by a fractional Ornstein-Uhlenbeck process with Hurst parameter $H<1/2$. Our methods are based on an equidistant discretization of the…

Probability · Mathematics 2016-06-14 Andreas Neuenkirch , Taras Shalaiko

We find Gaussian cutoff profiles for the total variation distance to stationarity of a random walk on a multiplex network: a finite number of directed configuration models sharing a vertex set, each with its own bounded degree distribution…

Probability · Mathematics 2024-03-19 John Fernley , Balázs Gerencsér

We give a sufficient condition under which the time-marginal law of $\mu$-reversible infinite interacting Brownian motions is characterised as the steepest gradient descent of the relative entropy in the Wasserstein space in the sense of…

Probability · Mathematics 2025-12-02 Kohei Suzuki

In this paper, we explore quantitative stability of multi-marginal Schr\"odinger bridges with respect to the marginal constraints. We focus on the case where the number of marginal constraints is large (i.e. ``many-marginals"). When this…

Probability · Mathematics 2026-04-17 Rentian Yao , Young-Heon Kim , Geoffrey Schiebinger

We obtain Fisher-Hartwig asymptotics with root and jump type singularities in space-time under the law of the stationary Hermitian Ornstein-Uhlenbeck process, which serve as a dynamical generalization of earlier static results obtained by…

Probability · Mathematics 2025-08-18 Ahmet Keles

This paper deals with the Local Asymptotical normality for the joint drift parameter and Hurst parameter $H>3/4$ in the mixed fractional Ornstein-Uhlenbeck process. Different from the only estimation of the drift parameter when $H$ is…

Probability · Mathematics 2025-10-21 Chunhao Cai , Cong Zhang

In this manuscript, we study the stability of the origin for the multivariate geometric Brownian motion. More precisely, under suitable sufficient conditions, we construct a Lyapunov function such that the origin of the multivariate…

Probability · Mathematics 2025-05-27 Gerardo Barrera , Eyleifur Bjarkason , Sigurdur Hafstein

We consider Langevin equation involving fractional Brownian motion with Hurst index $H\in(0,\frac12)$. Its solution is the fractional Ornstein-Uhlenbeck process and with unknown drift parameter $\theta$. We construct the estimator that is…

Probability · Mathematics 2015-01-20 Kestutis Kubilius , Yuliya Mishura , Kostiantyn Ralchenko , Oleg Seleznjev

We prove first-order convergence of semi-discrete monotone finite difference schemes for Hamilton--Jacobi equations on the Wasserstein space over a finite graph. A central challenge is the boundary degeneracy of the Wasserstein simplex,…

Numerical Analysis · Mathematics 2026-05-22 Jianbo Cui , Tonghe Dang

We develop an exactly-solvable three-state discrete-time minimal Brownian ratchet (MBR), where the transition probabilities between states are asymmetric. By solving the master equations we obtain the steady-state probabilities. Generally…

Statistical Mechanics · Physics 2009-11-07 Youngki Lee , Andrew Allison , Derek Abbott , H. Eugene Stanley

We study the long-time behaviour of solutions to a class of $d$-dimensional stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H \in (0,1)$. The drift consists of a dissipative Lipschitz term and a…

Probability · Mathematics 2025-12-23 Konstantinos Dareiotis , El Mehdi Haress , Khoa Lê

We give an explicit description of the jointly invariant measures for the KPZ equation. These are couplings of Brownian motions with drift, and can be extended to a process defined for all drift parameters simultaneously. We term this…

Probability · Mathematics 2025-07-15 Sean Groathouse , Firas Rassoul-Agha , Timo Seppäläinen , Evan Sorensen

Inverse problems in physical or biological sciences often involve recovering an unknown parameter that is random. The sought-after quantity is a probability distribution of the unknown parameter, that produces data that aligns with…

Machine Learning · Statistics 2024-10-02 Qin Li , Maria Oprea , Li Wang , Yunan Yang

We study boundary inference at $H=3/4$ for mixed fractional Brownian motion and mixed fractional Ornstein--Uhlenbeck models under high-frequency observation. This boundary is economically important because it separates the critical and…

Statistics Theory · Mathematics 2026-04-03 Chunhao Cai , Yiwu Shang , Weilin Xiao , Cong Zhang

The Boltzmann equation is a nonlinear partial differential equation that plays a central role in statistical mechanics. From the mathematical point of view, the existence of global smooth solutions for arbitrary initial data is an…

Analysis of PDEs · Mathematics 2020-11-25 Cyril Imbert , Luis Silvestre

Einstein-Smoluchowski diffusion, damped harmonic oscillations, and spatial decoherence are special cases of an elegant class of Markovian quantum Brownian motion models that is invariant under linear symplectic transformations. Here we…

Quantum Physics · Physics 2016-02-04 C. Jess Riedel

Sliced Wasserstein distances preserve properties of classic Wasserstein distances while being more scalable for computation and estimation in high dimensions. The goal of this work is to quantify this scalability from three key aspects: (i)…

Machine Learning · Statistics 2022-10-18 Sloan Nietert , Ritwik Sadhu , Ziv Goldfeld , Kengo Kato