This is a survey of the main methods in non-uniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, a...

The sample mean is one of the most natural estimators of the population mean based on independent identically distributed sample. However, if some control variate is available, it is known that the control variate method reduces the variance of the sample mean. The control variate method often assumes that the variable of intersest and the control variable are i.i.d. Here we assume that these v...

We prove that any multi-variate Hasse-Schmidt derivation can be decomposed in terms of substitution maps and uni-variate derivations. As a consequence we the bracket two $m$-integrable derivations is also $m$-integrable, for $m$ positive integer or infinity.

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind d...

The conventional control variate method proposed by Kemna and Vorst (1990) to evaluate Asian options under the Black-Scholes model can be interpreted as a particular selection of linear martingale controls. We generalize the constant control parameter into a control process to gain more reduction on variance. By means of an option price approximation, we construct a martingale control variate m...

This paper details a new control-variate splitting scheme yielding an unbiased esti-mator of the mean response and an unbiased estimator of the variance of the first estimator. This scheme also yields an asymptotically exact confidence interval for the mean response. We present analytical and empirical performance comparisons of this scheme versus other control-variate procedures.

Motivated by a problem arising in the regenerative analysis of discrete-event system simulation, we ask whether a certain class of random variate generation schemes exists or not. Under very reasonable conditions, we prove that such variate generation schemes do not exist. The implications of this result for regenerative steady-state simulation of discrete-event systems are discussed.