We leverage proof techniques from discrete Fourier analysis and an existing result in coding theory to derive new bounds for the problem of non-interactive simulation binary random variables. Previous literature were derived by applying data processing inequalities concerning maximal correlation or hypercontractivity. show that our are sharp some regimes. Indeed, a specific instance parameters,...

A new efficient technique to impose the statistical correlation when using Monte Carlo type method for statistical analysis of computational problems is proposed. The technique is based on stochastic optimization method called Simulated Annealing. The comparison with other techniques presently used and intensive numerical testing showed the superiority and robustness of the method. No significa...

• Usually we don’t have enough trace data to collect meaningful system measurements (statistically significant ones). • Sometimes we might purposely want to generate a Poisson arrival process, so that we can study effects of other variables. • If you’ve used empirical data to create closed-form analytic curve-fit, that analytic distribution can be very accurate. For example: the SURGE Web workl...

This paper describes a method for the Monte Carlo simulation of two correlated random variables. The author analyses linear combinations of stochastically independent random variables that are equally distributed over the interval (0; 1) (\random numbers") and also examines their distribution. If a suitable matrix of coeecients is chosen, the subsequent transformation results in random variable...

We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure.

Both the simulation research and software communities have been interested in optimization via simulation (OvS), by which we mean maximizing or minimizing the expected value of some output of a stochastic simulation. Continuous-decision-variable OvS, and gradient estimation to support it, has been an active research area with significant advances. However, the decision variables in many operati...