A comparison of vectorizable discrete sampling methods in Monte Carlo applications
The performance of various vectorizable discrete random-sampling methods, along with the commonly used inverse sampling method, is assessed on a vector machine. Monte Carlo applications involving, one-dimensional, two-dimensional and multi-dimensional probability tables are used in the investigation. Various forms of the weighted sampling method and methods that transform the original probability table are examined. It is found that some form of weighted sampling is efficient, when the original probability distribution is not far from uniform or can be approximated analytically. Table transformation methods, though require additional memory storage, are best suited in applications where multi-dimensional tables are involved. Keywords: Discrete sampling, Weighted sampling, Monte Carlo simulations, Vector processing.