#Modified latin hypercube sampling driver#

Encouraging results and good agreement between theory and simulation results have thus far been obtained.

#Modified latin hypercube sampling driver#

Finally, a numerical and a CMOS clock driver circuit examples are given. A few earlier studies explored the potential of sparse grid quadrature (SGQ), but this technique suffers due to. Quasi-Monte Carlo (QMC) methods such as Halton sequences or Modified Latin Hypercube Sampling (MLHS) are generally used to approximate this integral. Theoretical and practical aspects of its statistical properties are also given. Maximum likelihood estimation of mixed multinomial logit (MMNL) or probit models requires evaluation of a multidimensional integral. In this paper a variant of the Latin Hypercube Sampling MC method is presented which is an efficient variance reduction technique in MC estimation. The Monte Carlo (MC) method exhibits generality and insensitivity to the number of stochastic variables, but it is expensive for accurate Average Quality Index (AQI) or Parametric Yield estimation of MOS VLSI circuits or discrete component circuits. A Latin hypercube is the generalisation of this concept to an arbitrary. A square grid containing possible sample points is a Latin square iff there is only one sample in each row and each column. Analysis of the flux data shows that majority of. Latin hypercube sampling (LHS) was developed to generate a distribution of collections of parameter values from a multidimensional distribution. This paper proposes a modified method of Latin Hypercube sampling to reduce the variance of statistical parameters in uncertainty analysis of concrete. Modified Latin Hypercube Sampling Monte Carlo (MLHSMC) Estimation for Average Quality Index Modified Latin Hypercube Sampling Monte Carlo (MLHSMC) Estimation for Average Quality Index Sampling of the optimal flux space using modified LHS gives a more uniform coverage than Monte-Carlo Sampling.