Monte Carlo integration is often used for antialiasing in rendering processes. Due to low sampling rates only expected error estimates can be stated, and the variance can be high. In this article quasi-Monte Carlo methods are presented, achieving a guaranteed upper error bound and a convergence rate essentially as fast as usual Monte Carlo.

monte carlo methods designed base on stochastic reiterations and can be useful in uncertainty conditions. the purpose of this research was investigation of uncertainty to rank desertification indicators. in order to decrease uncertainty have been applied monte carlo reiterations for each value of indicator, if rank of xi is higher than rank of xk constantly. using multi dimension integrals the ...

This paper compares fault position and Monte Carlo methods as the most common methods in stochastic assessment of voltage sags. To compare their abilities, symmetrical and unsymmetrical faults with different probability distribution of fault positions along the lines are applied in a test system. The voltage sag magnitude in different nodes of test system is calculated. The problem with the...

We study the approximation of d-dimensional integrals. We present sufficient conditions for fast quasi-Monte Carlo convergence. They apply to isotropic and non-isotropic problems and, in particular, to a number of problems in computational finance. We show that the convergence rate of quasi-Monte Carlo is of order n−1+p{logn} −1/2 with p ≥ 0. This is a worst case result. Compared to the expecte...