A new class of stochastic Runge–Kutta methods for the weak approximation of the solution
of Itô stochastic differential equation systems with a multidimensional Wiener process is …

We perform a general optimization of the parameters in the multilevel Monte Carlo (MLMC)
discretization hierarchy based on uniform discretization methods with general approximation …

In this study, we present an adaptive multilevel Monte Carlo (AMLMC) algorithm for
approximating deterministic, real-valued, bounded linear functionals that depend on the …

This work generalizes a multilevel forward Euler Monte Carlo method introduced in Michael
B. Giles.(Michael Giles. Oper. Res. 56 (3): 607–617, 2008.) for the approximation of …

We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of
solutions to Itô stochastic differential equations (SDE). The work [Oper. Res. 56 (2008), 607 …

We derive a posteriori error estimates for the (stopped) weak Euler method to discretize SDE
systems which emerge from the probabilistic reformulation of elliptic and parabolic (initial) …

We study pathwise approximation of scalar stochastic differential equations at a single time
point or globally in time by means of methods that are based on finitely many observations of …

Physicists generally express the motion of objects in continuous time using differential
equations, whereas the majority of target tracking algorithms use discrete-time models. This …

Recently, it has been shown in Hairer et al.(2015) that there exists a system of stochastic
differential equations (SDE) on the time interval [0, T] with infinitely often differentiable and …

The weak approximation of the solution of a system of Stratonovich stochastic differential
equations with am–dimensional Wiener process is studied. Therefore, a new class of …