ABSTRACT
One of the important applications of the Monte Carlo method is estimation of integrals of functions or of physical quantities. The method is highly exible, and generally used for solving complex high-dimensional integrals or determining the outcomes of complex physical processes. The major issue is the need for signicant computation time for achieving an acceptable precision or uncertainty. Therefore, over the past several decades, signicant efforts have been allocated to the development of variance reduction approaches that result in smaller variance in a shorter computation time. Numerous articles and books from different scientic communities address techniques with varying success for different applications [Dunn and Shultis, 2012; Kalos and Whitlock, 2008; Liu, 2008; Spanier and Gelbard, 2008; and Glasserman, 2003].
