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Stochastic gradient descent is a classic algorithm that has gained great popularity especially in the last decades as the most common approach for training models in machine learning. While the algorithm has been well-studied when…

Machine Learning · Statistics 2025-09-09 Jose Blanchet , Aleksandar Mijatović , Wenhao Yang

In this paper we develop a framework for estimating Probability of Default (PD) based on stochastic models governing an appropriate asset value processes. In particular, we build upon a L\'evy-driven Ornstein-Uhlenbeck process and consider…

Risk Management · Quantitative Finance 2023-09-25 Kyriakos Georgiou , Athanasios N. Yannacopoulos

We define a time-changed fractional Ornstein-Uhlenbeck process by composing a fractional Ornstein-Uhlenbeck process with the inverse of a subordinator. Properties of the moments of such process are investigated and the existence of the…

Probability · Mathematics 2020-04-30 Giacomo Ascione , Yuliya Mishura , Enrica Pirozzi

We consider the operator $$\sL f(x)=\tfrac12 \sum_{i,j=1}^\infty a_{ij}(x)\frac{\del^2 f}{\del x_i \del x_j}(x)-\sum_{i=1}^\infty \lam_i x_i b_i(x) \frac{\del f}{\del x_i}(x).$$ We prove existence and uniqueness of solutions to the…

Probability · Mathematics 2007-05-23 Siva R. Athreya , Richard F. Bass , Maria Gordina , Edwin A. Perkins

This paper studies Langevin equation with random damping due to multiplicative noise and its solution. Two types of multiplicative noise, namely the dichotomous noise and fractional Gaussian noise are considered. Their solutions are…

Statistical Mechanics · Physics 2017-11-30 Chai Hok Eab , S. C. Lim

Conditions are given, sufficient for the distribution of an Ornstein-Uhlenbeck process with L\'evy noise to be absolutely continuous or to possess a smooth density. For the processes with non-degenerate drift coefficient, these conditions…

Probability · Mathematics 2008-06-04 Semen V. Bodnarchuk , Alexey M. Kulik

In this paper we introduce and investigate a new kind of functional (including ordinary and evolutionary partial) differential equations. The main goal of this paper is to explore our new philosophy by some examples on functional ODEs and…

Analysis of PDEs · Mathematics 2014-02-14 De-Xing Kong , Cheng Zhang

We demonstrate that two Ornstein--Uhlenbeck processes, that is, solutions to certain stochastic differential equations that are driven by a L\'evy process L have equivalent laws as long as the eigenvalues of the covariance operator…

Probability · Mathematics 2019-05-14 Grzegorz Bartosz , Tomasz Kania

Ornstein-Uhlenbeck process of bounded variation is introduced as a solution of an analogue of the Langevin equation with an integrated telegraph process replacing a Brownian motion. There is an interval $I$ such that the process starting…

Probability · Mathematics 2020-07-17 Nikita Ratanov

The Ornstein-Uhlenbeck (OU) process describes the dynamics of Brownian particles in a confining harmonic potential, thereby constituting the paradigmatic model of overdamped, mean-reverting Langevin dynamics. Despite its widespread…

Statistical Mechanics · Physics 2024-05-16 Luca Cocconi , Henry Alston , Jacopo Romano , Thibault Bertrand

We use the semi-discrete method, originally proposed in Halidias (2012), Semi-discrete approximations for stochastic differential equations and applications, International Journal of Computer Mathematics, 89(6), to reproduce qualitative…

Numerical Analysis · Mathematics 2017-08-29 Ioannis S. Stamatiou

We consider the goodness of fit testing problem for linear stochastic differential equation (Ornstein-Uhlenbeck process). The basic hypothesis is supposed to be composite with two-dimensional unknown parameter. We study two goodness of fit…

Statistics Theory · Mathematics 2013-05-16 Yury A. Kutoyants

We study stochastic evolution equations describing the dynamics of open quantum systems. First, using resolvent approximations, we obtain a sufficient condition for regularity of solutions to linear stochastic Schroedinger equations driven…

Quantum Physics · Physics 2014-05-27 Franco Fagnola , Carlos M. Mora

Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution. We show…

Machine Learning · Statistics 2017-09-12 Stephan Mandt , Matthew D. Hoffman , David M. Blei

We study a class of linear ordinary differential equations (ODE)s with distributional coefficients. These equations are defined using an {\it intrinsic} multiplicative product of Schwartz distributions which is an extension of the…

Classical Analysis and ODEs · Mathematics 2021-11-09 Nuno Costa Dias , Cristina Jorge , Joao Nuno Prata

We prove averaging theorems for ordinary differential equations and retarded functional differential equations. Our assumptions are weaker than those required in the results of the existing literature. Usually, we require that the…

Dynamical Systems · Mathematics 2007-05-23 Mustapha Lakrib , Tewfik Sari

In this work, some regularity properties of mild solutions for a class of stochastic linear functional differential equations driven by infinite dimensional Wiener processes are considered. In terms of retarded fundamental solutions, we…

Probability · Mathematics 2011-06-09 Kai Liu

We investigate the distributional properties of two generalized Ornstein-Uhlenbeck (OU) processes whose stationary distributions are the gamma law and the bilateral gamma law, respectively. The said distributions turn out to be related to…

Probability · Mathematics 2021-03-25 Nicola Cufaro Petroni , Piergiacomo Sabino

We present the correct space of test functions for the Ornstein-Uhlenbeck processes defined in \cite{fgn2}. Under these new spaces, an invariance with respect to a second order operator is shown, granting the existence and uniqueness of…

Probability · Mathematics 2016-07-01 Tertuliano Franco , Patrícia Gonçalves , Adriana Neumann

There is a whole range of emergent phenomena in non-equilibrium behaviors can be well described by a set of stochastic differential equations. Inspired by an insight gained during our study of robustness and stability in phage lambda…

Other Condensed Matter · Physics 2016-09-08 P. Ao