The authors propose a two-timescale version of the one-simulation smoothed functional (SF) algorithm with extra averaging. They also propose the use of a chaotic simple deterministic iterative sequence for generating random samples for averaging. This sequence is used for generating the N independent and identically distributed (i.i.d.), Gaussian random variables in the SF algorithm. The conver...

A method for generating random U(1) variables with Boltzmann distribution is presented. It is based on the rejection method with transformation of variables. High efficiency is achieved for all range of temparatures or coupling parameters, which makes the present method especially suitable for parallel and pipeline vector processing machines. Results of computer runs are presented to illustrate...

Suppose we wish to estimate some quantity, θ = E[h(X)], where X = {X 1 ,. .. , X n } is a random vector in R n , h(·) is a function from R n to R, and E[|h(X)|] < ∞. Note that X could represent the values of a stochastic process at different points in time. For example, X i might be the price of a particular stock at time i and h(·) might be given by h(X) = X 1 +. .. + X n n so then θ is the ex...

The distribution of ratio of two random variables has been studied by several authors especially when the two random variables are independent and come from the same family. In this paper, the exact distribution of the ratio of two independent HyperErlang distribution is derived. However, closed expressions of the probability density, cumulative distribution function, reliability function, haza...

We here present a general and tractable model for line-of-sight (LOS) scenarios, which is based on the product of two independent and non-identically distributed κ-μ shadowed random variables. Simple closed-form expressions for the probability density function, cumulative distribution function and moment-generating function are derived, which are as tractable as the corresponding expressions de...