![]() Large Sample Properties of Simulations Using Latin Hypercube Sampling, Michael Stein, Technometrics, Vol. Owen, 1992, Journal of the Royal Statistical Society. ![]() The first result is a Berry-Esseen-type bound for the multivariate central limit. Notz, Springer Verlag, New York 2003Ī Central Limit Theorem for Latin Hypercube Sampling, Art B. This paper contains a collection of results on Latin hypercube sampling. The Design and Analysis of Computer Experiments by Thomas J. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems, J. Mc Kay, Conover, Beckman, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21 (2), 1979 Latin Hypercube sampling is just one of many mathematical models working behind the scenes to power the library of Cadence’s PCB design and analysis software, and the best part is that you don’t need to be a mathematician to gain their benefits in your PCB design suite. See Estimate a probability with Latin Hypercube Sampling A simulation study had performed on hypercube computer to bring the optimization for (i) the distribution timing of workloads among the neighbor processors from server or originating processor. This method is derived from a more general method called ’Stratified Hypercube Simulation Algorithm Performance Source publication Dynamic entity distribution in parallel discrete event simulation Conference Paper Full-text available Dec 2008 Michael Slavik. ![]() This item may be available elsewhere in EconPapers: Search for items with the same title.It gives an unbiased estimate for (reminding that all input The axis (set of fixed points) in a 4D rotation is a plane. References: View references in EconPapers View complete reference list from CitEcĬitations: View citations in EconPapers (13) Track citations by RSS feed This Demonstration gives a variety of animated rotations of a hypercube in 4D projected to 3D. JEL-codes: C15 C63 G12 (search for similar items in EconPapers) Keywords: Monte Carlo simulation variance reduction Latin hypercube sampling stratified sampling (search for similar items in EconPapers) Latin hypercube sampling is a recently developed sampling technique for generating input vectors into computer models for purposes of sensitivity analysis. LHSD is suited for problems with rare events and for high-dimensional problems, and it may be combined with Quasi-Monte Carlo methods. In some valuation examples of financial payoffs, when compared to standard Monte Carlo simulation, a variance reduction of factors up to 200 is achieved. It is shown that for a class of estimators satisfying some monotonicity condition, the LHSD limit variance is never greater than the corresponding Monte Carlo limit variance. For the bivariate case and under some conditions on the joint distribution, a central limit theorem together with a closed formula for the limit variance are derived. The resulting estimator is shown to be consistent and asymptotically unbiased. The analysis shows that, in this application, the Modified Latin Hypercube Sampling (MLHS) outperforms each type of Halton sequence. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random variables. In Monte Carlo simulation, Latin hypercube sampling (LHS) is a well-known variance reduction technique for vectors of independent random variables. No 15, CPQF Working Paper Series from Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF) We will only consider the case where the components of x are independent or can be transformed into an independent base. The sampling region is partitioned into a specific manner by dividing the range of each component of x. LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. The Latin Hypercube Sampling (LHS) is a type of stratified Monte Carlo (MC). ![]() The sampling methodis often used to construct computer experimentsor for Monte Carlo integration. Latin hypercube sampling with dependence and applications in finance Latin hypercube sampling(LHS) is a statisticalmethod for generating a near-random sample of parameter values from a multidimensional distribution.
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