Numerical integration of stochastic differential equations together with the Monte Carlo technique is used to evaluate conditional Wiener integrals of exponential-type functionals. An explicit Runge-Kutta method of order four and implicit Runge-Kutta methods of order two are constructed. The corresponding convergence theorems are proved. To reduce the Monte Carlo error, a variance reduction technique is considered. Results of numerical experiments are presented
We present a new pathwise approximation method for stochastic differential equations driven by Brow...
We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multiv...
The Ito-Stratonovich theory of stochastic integration and stochastic differential equations has seve...
A numerical method of second order of accuracy for computing conditional Wiener integrals of smooth ...
The path-integral formulation of nonrelativistic quantum mechanics was introduced by Feynman in 1948...
The path-integral formulation of nonrelativistic quantum mechanics was introduced by Feynman in 1948...
Introduction to numerical methods to simulate systems of stochastic differential equations (SDEs) bo...
AbstractA new algorithm of first order is proposed for the numerical solution of linear Ito stochast...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
Introduction Deterministic calculus is much more robust to approximation than stochastic calculus b...
In a number of problems of mathematical physics and other fields stochastic differential equations a...
Often when solving stochastic differential equations numerically, many simulations must be generated...
AbstractNew approximate formulae for functional integrals with Gaussian measure in separable Frechét...
An adaptive stepsize algorithm is implemented on a stochastic implicit strong order 1 method, namely...
This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic...
We present a new pathwise approximation method for stochastic differential equations driven by Brow...
We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multiv...
The Ito-Stratonovich theory of stochastic integration and stochastic differential equations has seve...
A numerical method of second order of accuracy for computing conditional Wiener integrals of smooth ...
The path-integral formulation of nonrelativistic quantum mechanics was introduced by Feynman in 1948...
The path-integral formulation of nonrelativistic quantum mechanics was introduced by Feynman in 1948...
Introduction to numerical methods to simulate systems of stochastic differential equations (SDEs) bo...
AbstractA new algorithm of first order is proposed for the numerical solution of linear Ito stochast...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
Introduction Deterministic calculus is much more robust to approximation than stochastic calculus b...
In a number of problems of mathematical physics and other fields stochastic differential equations a...
Often when solving stochastic differential equations numerically, many simulations must be generated...
AbstractNew approximate formulae for functional integrals with Gaussian measure in separable Frechét...
An adaptive stepsize algorithm is implemented on a stochastic implicit strong order 1 method, namely...
This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic...
We present a new pathwise approximation method for stochastic differential equations driven by Brow...
We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multiv...
The Ito-Stratonovich theory of stochastic integration and stochastic differential equations has seve...