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In this paper, we address high-dimensional parametric estimation of the drift function in diffusion models, specifically focusing on a $d$-dimensional ergodic diffusion process observed at discrete time points. We consider both a general…

Statistics Theory · Mathematics 2025-10-09 Chiara Amorino , Francisco Pina , Mark Podolskij

We introduce a novel linear transport equation that models the evolution of a one-particle distribution subject to free transport and two distinct scattering mechanisms: one affecting the particle's speed and the other its direction. These…

Mathematical Physics · Physics 2025-11-06 Martina Conte , Nadia Loy

We consider a pure-jump stable Cox-Ingersoll-Ross ($\alpha$-stable CIR) process driven by a non-symmetric stable L{\'e}vy process with jump activity $\alpha$ $\in$ (1, 2) and we address the joint estimation of drift, scaling and jump…

Probability · Mathematics 2024-02-13 Elise Bayraktar , Emmanuelle Clément

Inspired by holographic Wilsonian renormalization, we consider coarse graining a quantum system divided between short distance and long distance degrees of freedom, coupled via the Hamiltonian. Observations using purely long distance…

High Energy Physics - Theory · Physics 2018-08-01 Cesar Agon , Vijay Balasubramanian , Skyler Kasko , Albion Lawrence

Recently, the complex Langevin method has been applied successfully to finite density QCD either in the deconfinement phase or in the heavy dense limit with the aid of a new technique called the gauge cooling. In the confinement phase with…

High Energy Physics - Lattice · Physics 2016-08-24 Keitaro Nagata , Jun Nishimura , Shinji Shimasaki

I investigate the Kazakov-Migdal (KM) model -- the Hermitean gauge-invariant matrix model on a D-dimensional lattice. I utilize an exact large-N solution of the KM model with a logarithmic potential to examine its critical behavior. I find…

High Energy Physics - Theory · Physics 2009-10-28 Yu. Makeenko

We propose a change detection method for the famous Cox--Ingersoll--Ross model. This model is widely used in financial mathematics and therefore detecting a change in its parameters is of crucial importance. We develop one- and two-sided…

Statistics Theory · Mathematics 2015-02-26 Gyula Pap , Tamás T. Szabó

We investigate how the dimensionless ratio given by the Chern-Simons diffusion rate $\Gamma_{\textrm{CS}}$ divided by the product of the entropy density $s$ and temperature $T$ behaves across different kinds of phase transitions in the…

High Energy Physics - Phenomenology · Physics 2016-05-26 Romulo Rougemont , Stefano Ivo Finazzo

We consider the minor process of (Hermitian) matrix diffusions with constant diagonal drifts. At any given time, this process is determinantal and we provide an explicit expression for its correlation kernel. This is a measure on the…

Mathematical Physics · Physics 2014-04-24 Patrik L. Ferrari , René Frings

We present a unified framework to efficiently approximate solutions to fractional diffusion problems of stationary and parabolic type. After discretization, we can take the point of view that the solution is obtained by a matrix-vector…

Numerical Analysis · Mathematics 2021-04-19 Tobias Danczul , Clemens Hofreither , Joachim Schöberl

We present first elements of an extension of Yakhot's model of strong turbulence towards small scales. The analysis is based on an empirically observed relation for even order structure functions which extends from the inertial into the…

Fluid Dynamics · Physics 2026-04-20 Christoph Renner

In this paper, we establish new quantitative convergence bounds for a class of functional autoregressive models in weighted total variation metrics. To derive our results, we show that under mild assumptions, explicit minorization and…

Probability · Mathematics 2020-05-05 Valentin De Bortoli , Alain Durmus

In this paper, we prove pathwise uniqueness for stochastic systems of McKean-Vlasov type with singular drift, even in the measure argument, and uniformly non-degenerate Lipschitz diffusion matrix. Our proof is based on Zvonkin's…

Probability · Mathematics 2016-03-04 Paul-Eric Chaudru de Raynal

In this paper we display a family of Gaussian processes, with explicit formulas and transforms. This is presented with the use of duality tools in such a way that the corresponding path-space measures are mutually singular. We make use of a…

Mathematical Physics · Physics 2022-04-27 Daniel Alpay , Palle Jorgensen , Motke Porat

This paper discusses model order reduction of large sparse second-order index-3 differential algebraic equations (DAEs) by applying Iterative Rational Krylov Algorithm (IRKA). In general, such DAEs arise in constraint mechanics, multibody…

Optimization and Control · Mathematics 2021-01-11 Xin Du , M. Monir Uddiny , A. Mostakim Fonyz , Md. Tanzim Hossainx , Md. Nazmul Islam Shuzan

Motivated by a probabilistic approach to Kahler-Einstein metrics we consider a general non-equilibrium statistical mechanics model in Euclidean space consisting of the stochastic gradient flow of a given (possibly singular) quasi-convex…

Mathematical Physics · Physics 2016-10-17 Robert J. Berman , Magnus Onnheim

We present an effective field theory for a unified description of transport in normal and superconducting metals in the presence of generic spin-orbit coupling (SOC). The structure of the quantum kinetic theory in the diffusive regime is…

Superconductivity · Physics 2025-01-28 Tim Kokkeler , F. Sebastian Bergeret , Ilya Tokatly

A general method is proposed which allows one to estimate drift and diffusion coefficients of a stochastic process governed by a Langevin equation. It extends a previously devised approach [R. Friedrich et al., Physics Letters A 271, 217…

Data Analysis, Statistics and Probability · Physics 2009-11-11 D. Kleinhans , R. Friedrich , A. Nawroth , J. Peinke

Cross-sectional observations from a dynamical system can be modeled via steady-state distributions of Markov processes. The major challenge is then to determine whether the process parameters can be identified and estimated from the…

Statistics Theory · Mathematics 2026-03-19 Cecilie Olesen Recke , Niels Richard Hansen

Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coefficients from data remains challenging. Standard approaches often rely on short-time…

Machine Learning · Statistics 2026-04-28 Ludovico T. Giorgini