Multiple stochastic integrals of higher multiplicity cannot always be expressed in terms of simpler stochastic integrals, especially when the Wiener process is multidimensional. In this paper we describe how the Fourier series expansion of Wiener process can be used to simulate a two-dimensional stochastic differential equation (SDE) using Matlab program. Our numerical experiments use Matlab to show how our truncation of Itô’-Taylor expansion at an appropriate point produces Milstein method for the SDE
In this article, we propose a Milstein finite difference scheme for a stochastic partial differentia...
AbstractWe develop some numerical schemes for d-dimensional stochastic differential equations derive...
AbstractA multi-dimensional system of stochastic differential equations is presented for modeling pr...
This article studies an infinite-dimensional analog of Milstein's scheme for finite-dimensional stoc...
Abstract In this paper we are concerned with numerical methods to solve stochastic differential equa...
In this paper, we consider the problem of computing numerical solutions for Ito stochastic different...
Stiff stochastic differential equations arise in many applications including in the area of biology....
In this paper, the strong approximation of a stochastic partial differential equation, whose differe...
Abstract. In this paper, we consider the problem of computing numerical solutions for stochastic dif...
Abstract: This paper examines the effect of varying stepsizes in finding the approximate solution of...
Introduction Deterministic calculus is much more robust to approximation than stochastic calculus b...
We study a stochastic integral that arises during the implementation of the Milstein method for the ...
This paper demonstrates a derivation of stochastic Taylor methods for stochastic differential equati...
We considered strong convergent stochastic schemes for the simulation of stochastic differential equ...
In this article, we propose a Milstein finite difference scheme for a stochastic partial differentia...
In this article, we propose a Milstein finite difference scheme for a stochastic partial differentia...
AbstractWe develop some numerical schemes for d-dimensional stochastic differential equations derive...
AbstractA multi-dimensional system of stochastic differential equations is presented for modeling pr...
This article studies an infinite-dimensional analog of Milstein's scheme for finite-dimensional stoc...
Abstract In this paper we are concerned with numerical methods to solve stochastic differential equa...
In this paper, we consider the problem of computing numerical solutions for Ito stochastic different...
Stiff stochastic differential equations arise in many applications including in the area of biology....
In this paper, the strong approximation of a stochastic partial differential equation, whose differe...
Abstract. In this paper, we consider the problem of computing numerical solutions for stochastic dif...
Abstract: This paper examines the effect of varying stepsizes in finding the approximate solution of...
Introduction Deterministic calculus is much more robust to approximation than stochastic calculus b...
We study a stochastic integral that arises during the implementation of the Milstein method for the ...
This paper demonstrates a derivation of stochastic Taylor methods for stochastic differential equati...
We considered strong convergent stochastic schemes for the simulation of stochastic differential equ...
In this article, we propose a Milstein finite difference scheme for a stochastic partial differentia...
In this article, we propose a Milstein finite difference scheme for a stochastic partial differentia...
AbstractWe develop some numerical schemes for d-dimensional stochastic differential equations derive...
AbstractA multi-dimensional system of stochastic differential equations is presented for modeling pr...