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In this paper, we study functional type weak approximation of weak solutions of stochastic functional differential equations by means of the Euler--Maruyama scheme. Under mild assumptions on the coefficients, we provide a quantitative error…

Probability · Mathematics 2024-12-25 Yushi Hamaguchi , Dai Taguchi

By analogy with the theory of Backward Stochastic Differential Equations, we define Backward Stochastic Difference Equations on spaces related to discrete time, finite state processes. This paper considers these processes as constructions…

Probability · Mathematics 2010-07-12 Samuel N. Cohen , Robert J. Elliott

To verify theoretical results it is sometimes important to use a numerical example where the solution has a particular regularity. The paper describes one approach to construct such examples. It is based on the regularity theory for…

Numerical Analysis · Mathematics 2025-03-10 Thomas Apel , Katharina Lorenz , Serge Nicaise

We consider the stochastic transport equation where the randomness is given by the symmetric integral with respect to stochastic measure. For stochastic measure, we assume only $\sigma$-additivity in probability and continuity of paths. The…

Probability · Mathematics 2024-09-11 Vadym Radchenko

In the present paper, we give some examples of stochastic differential equations which have delicateness in the Markov and strong Markov properties, the uniqueness locally in time and globally in time, and initial conditions. Moreover, we…

Probability · Mathematics 2022-09-14 Seiichiro Kusuoka

This article is an attempt to complement some recent developments on conservation laws with stochastic forcing. In a pioneering development, Feng $&$ Nualarthave developed the entropy solution theory for such problems and the presence of…

Analysis of PDEs · Mathematics 2014-08-28 Imran H. Biswas , Ananta K. Majee

We first introduce the concept of weak random periodic solutions of random dynamical systems. Then, we discuss the existence of such periodic solutions. Further, we introduce the definition of weak random periodic measures and study their…

Dynamical Systems · Mathematics 2022-08-03 Wei Sun , Zuo-Huan Zheng

A common statistical task lies in showing asymptotic normality of certain statistics. In many of these situations, classical textbook results on weak convergence theory suffice for the problem at hand. However, there are quite some…

Probability · Mathematics 2019-03-26 Viktor Bengs , Hajo Holzmann

The time evolution of the two-time conditional probability of the classical stochastic process is described in an analogous form of the quantum mechanical wave equations. By using it, we emulate the same strange behaviors as those of the…

Statistical Mechanics · Physics 2012-01-11 Hiroyuki Tomita

We study the stochastically forced system of isentropic Euler equations of gas dynamics with a $\gamma$-law for the pressure. We show the existence of martingale weak entropy solutions; we also discuss the existence and characterization of…

Analysis of PDEs · Mathematics 2015-12-18 Florent Berthelin , Julien Vovelle

We apply results of Malliavin-Thalmaier-Watanabe for strong and weak Taylor expansions of solutions of perturbed stochastic differential equations (SDEs). In particular, we work out weight expressions for the Taylor coefficients of the…

Computational Finance · Quantitative Finance 2008-12-10 Maria Siopacha , Josef Teichmann

This article proves the existence and regularity of weak solutions for a class of mixed local-nonlocal problems with singular nonlinearities. We examine both the purely singular problem and perturbed singular problems. A central…

Analysis of PDEs · Mathematics 2025-02-27 Sanjit Biswas , Prashanta Garain

The continuous dependence of solutions to certain (non-autonomous, partial, integro-differential-algebraic, evolutionary) equations on the coefficients is addressed. We give criteria that guarantee that convergence of the coefficients in…

Functional Analysis · Mathematics 2016-01-21 Marcus Waurick

This paper discusses stochastic numerical methods of Runge-Kutta type with weak and strong convergences for systems of stochastic differential equations in It\^o form. At the beginning we give a brief overview of the stochastic numerical…

Numerical Analysis · Computer Science 2018-11-06 Migran N. Gevorkyan , Anastasia V. Demidova , Anna V. Korolkova , Dmitry S. Kulyabov

The purpose of this work is to introduce a notion of weak solution to the master equation of a potential mean field game and to prove that existence and uniqueness hold under quite general assumptions. Remarkably, this is achieved without…

Optimization and Control · Mathematics 2022-06-30 Alekos Cecchin , François Delarue

Nearly-elastic model systems with one or two degrees of freedom are considered: the system is undergoing a small loss of energy in each collision with the "wall". We show that instabilities in this purely deterministic system lead to…

Probability · Mathematics 2012-08-31 Mark Freidlin , Wenqing Hu

We construct singular solutions to the Yamabe equation using a reduction of the problem in an equivariant setting. This provides a non-trivial geometric example for which the analysis is simpler than in Mazzeo-Pacard program. Our…

Analysis of PDEs · Mathematics 2020-05-20 Ali Hyder , Angela Pistoia , Yannick Sire

The outcome of a weak quantum measurement conditioned to a subsequent postselection (a weak value protocol) can assume peculiar values. These results cannot be explained in terms of conditional probabilistic outcomes of projective…

Quantum Physics · Physics 2016-05-31 Alessandro Romito , Andrew N. Jordan , Yakir Aharonov , Yuval Gefen

We consider systems of stochastic differential equations of the form \[ \d X_t^i = \sum_{j=1}^d A_{ij}(X_{t-}) \d Z_t^j\] for $i=1,\dots,d$ with continuous, bounded and non-degenerate coefficients. Here $Z_t^1,\dots,Z_t^d$ are independent…

Probability · Mathematics 2019-10-11 Jamil Chaker

We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodological framework where the modeled functions and derivatives…

Machine Learning · Statistics 2018-06-19 Marco Lorenzi , Maurizio Filippone