This chapter provides an introduction to the different approaches available for sampling from
a given probability distribution. We start with a brief history of the Monte Carlo (MC) method, …

… , our method of simple random sampling from intersections (Section 6), which entails sampling
… queries over the independent random variables. Large numbers of queries (eg 300) were …

… Assuming that a random sampling design p is used, we can take the statistical … This
view fails to recognize that independent random selection of sample locations generates p-…

… sample views [OR92a], and sampling from spatial databases [OR92b, OR93]. The emphasis
of this work has been on the algorithms to obtain random samples (usually simple random …

… on a new external sorting method called BucketSort that uses random sampling for preprocessing
[7]. One way to select the n records is to generate an independent random integer k …

… In principle this method requires two independent random numbers for each sampled value
of X, but we shall show that one of them is unnecessary. Assuming that the alias and cutoff …

… investigated, using sampling percentages of 0.2 percent, 0.5 percent, 1 percent, 3 percent,
and 5 percent. In place of SRS, an independent random sample (IRS), sampling with equal …

… In summary, Monte Carlo simulations were used instead of an independent t-test or ANOVA
to generate ap value for differences between means. The aspect of interest is the probability …

… We repeat this for k independent random vectors. If there is even one vector for which f
evaluates to a non zero value, then clearly f is nonzero. If f evaluated to zero on all the k vectors …

… The random sample consists of observations from a probability distribution that contains
controllable design parameters as well as other parameters modelling the data. …