![]() median of the interval conditional pdf instead of its mean value. V(Y) exp(2u + a)exp(o) - 1 Median As with the normal distribution n. Latin hypercube sampling (LHS) is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional. Key Words: Monte Carlo estimation, Latin hypercube sampling, average quality index. The selection of the intervals is performed in such a way that the. The present program replaces the previous Latin hypercube sampling program. Encouraging results and good agreement between theory and simulation results have thus far been obtained. Latin Hypercube Sampling For time-intensive calculations, small-sample simulation techniques based on stratified sampling of the Monte Carlo type represent a rational compromise between feasibility and accuracy. Finally, a numerical and a CMOS clock driver circuit examples are given. Theoretical and practical aspects of its statistical properties are also given. 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. Modified Latin Hypercube Sampling Monte Carlo (MLHSMC) Estimation for Average Quality Index Modified Latin Hypercube Sampling Monte Carlo (MLHSMC) Estimation for Average Quality Index ![]()
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