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Inspired by the stochastic particle method, this paper establishes an easily implementable explicit numerical method for McKean-Vlasov stochastic differential equations (MV-SDEs) with superlinear growth coefficients. The paper establishes…

Probability · Mathematics 2025-12-25 Yuanping Cui , Xiaoyue Li , Yi Liu , Fengyu Wang

In this paper we study the existence and uniqueness of the $L_{\rho}^{2p}(\mathbb{R}^d;\mathbb{R}^1)\times L_{\rho}^2(\mathbb{R}^d;\mathbb{R}^d)$ valued solution of backward doubly stochastic differential equations with polynomial growth…

Probability · Mathematics 2011-11-10 Qi Zhang , Huaizhong Zhao

This paper studies a class of mean-field control (MFC) problems with singular controls under general dynamic state-control-law constraints. We first propose a customized relaxed control formulation to cope with the dynamic mixed constraints…

Optimization and Control · Mathematics 2026-04-28 Lijun Bo , Jingfei Wang , Xiang Yu

We study stochastic delay differential equations (SDDE) where the coefficients depend on the moving averages of the state process. As a first contribution, we provide sufficient conditions under which a linear path functional of the…

Probability · Mathematics 2013-10-17 Salvatore Federico , Peter Tankov

We consider multidimensional quadratic BSDEs with bounded and unbounded terminal conditions. We provide sufficient conditions which guarantee existence and uniqueness of solutions. In particular, these conditions are satisfied if the…

Probability · Mathematics 2017-10-24 Asgar Jamneshan , Michael Kupper , Peng Luo

We develop a general method for extending Markov processes to a larger state space such that the added points form a polar set. The so obtained extension is an improvement on the standard trivial extension in which case the process is made…

Probability · Mathematics 2019-09-06 Lucian Beznea , Iulian Cîmpean , Michael Röckner

This paper presents three versions of maximum principle for a stochastic optimal control problem of Markov regime-switching forward-backward stochastic differential equations with jumps (FBSDEJs). A general sufficient maximum principle for…

Optimization and Control · Mathematics 2014-10-14 Olivier Menoukeu Pamen

We study backward stochastic differential equations (BSDEs) in infinite horizon and design efficient numerical schemes for solving them. We establish a probabilistic representation of the solution of the BSDE using Malliavin derivative and…

Probability · Mathematics 2026-04-28 Emmanuel Gobet , Adrien Richou , Charu Shardul

We present a flexible Bayesian semiparametric mixed model for longitudinal data analysis in the presence of potentially high-dimensional categorical covariates. Building on a novel hidden Markov tensor decomposition technique, our proposed…

Methodology · Statistics 2022-08-05 Giorgio Paulon , Peter Müller , Abhra Sarkar

In this paper we study stochastic optimal control problems of fully coupled forward-backward stochastic differential equations (FBSDEs). The recursive cost functionals are defined by controlled fully coupled FBSDEs. We study two cases of…

Optimization and Control · Mathematics 2013-02-06 Juan Li , Qingmeng Wei

We introduce a multivariate multidimensional mixed-effects regression model in a finite mixture framework. We relax the usual unidimensionality assumption on the random effects multivariate distribution. Thus, we introduce a…

Methodology · Statistics 2014-10-20 Alessandra Marcelletti , Antonello Maruotti , Giovanni Trovato

This paper is devoted to the general solvability of anticipated backward stochastic differential equations with quadratic growth by relaxing the assumptions made by Hu, Li, and Wen \cite[Journal of Differential Equations, 270 (2021),…

Probability · Mathematics 2025-05-22 Ying Hu , Feng Li , Jiaqiang Wen

In this paper, we study a class of infinite horizon fully coupled McKean-Vlasov forward-backward stochastic differential equations (FBSDEs). We propose a generalized monotonicity condition involving two flexible functions. Under this…

Optimization and Control · Mathematics 2024-03-28 Tianjiao Hua , Peng Luo

Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dynamics have prompted a number of theoretical advances; despite…

Machine Learning · Statistics 2020-06-24 Adam Block , Youssef Mroueh , Alexander Rakhlin , Jerret Ross

In this paper we study a multidimensional quadratic BSDE with a particular class of product generators and give a result of existence of solution in a suitable complete metric space under some constraints on parameters. We also use that…

Probability · Mathematics 2019-05-02 Zhongmin Qian , Shujin Wu , Yimin Yang

In this paper, we study the well-posedness of backward doubly stochastic differential equations (BDSDEs), both with and without reflection, under weak conditions. First, when the generator $f$ is of general growth in $y$ and linear growth…

Probability · Mathematics 2026-03-17 Shuxian Gao , Ying Hu , Jiaqiang Wen

This paper is concerned with a class of mean-field type coupled forward-backward stochastic differential equations (MF-FBSDEs, for short), in which the coupling appears in integral terms, terminal terms, and initial terms. Inspired by…

Optimization and Control · Mathematics 2022-03-29 Ran Tian , Zhiyong Yu

This paper is devoted to the existence, uniqueness and comparison theorem on unbounded solutions of one-dimensional backward stochastic differential equations (BSDEs) with sub-quadratic generators, where the terminal time is allowed to be…

Probability · Mathematics 2024-06-11 Chuang Gu , Yan Wang , Shengjun Fan

We study four systems and their interactions. First, we formulate a unified system of coupled forward-backward stochastic partial differential equations (FB-SPDEs) with Levy jumps, whose drift, diffusion, and jump coefficients may involve…

Probability · Mathematics 2015-09-15 Wanyang Dai

We propose several algorithms to solve McKean-Vlasov Forward Backward Stochastic Differential Equations. Our schemes rely on the approximating power of neural networks to estimate the solution or its gradient through minimization problems.…

Optimization and Control · Mathematics 2022-03-08 Maximilien Germain , Joseph Mikael , Xavier Warin