  This paper focuses on analyzing the error of the randomized Euler algorithm
when only noisy information about the coefficients of the underlying stochastic
differential equation (SDE) and the driving Wiener process is available. Two
classes of disturbed Wiener process are considered, and the dependence of the
algorithm's error on the regularity of the disturbing functions is
investigated. The paper also presents results from numerical experiments to
support the theoretical findings.

Numerical Analysis;Numerical Analysis