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# latin_hs - with an Application to Risk...

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function s=latin_hs(xmean,xsd,nsample,nvar) % s=latin_hs(xmean,xsd,nsample,nvar) % LHS from normal distribution, no correlation % method of Stein % Stein, M. 1987. Large Sample Properties of Simulations Using Latin Hypercube Sampling. % Technometrics 29:143-151 % Input: % xmean : mean of data (1,nvar) % xsd : std.dev of data (1,nvar) % nsample : no. of samples % nvar : no. of variables % Output: % s : random sample (nsample,nvar) % % Uses Peter Acklam inverse normal CDF % % Budiman (2003) % References: % Iman, R. L., and W. J. Conover. 1980. Small Sample Sensitivity Analysis Techniques for Computer Models,
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Unformatted text preview: % with an Application to Risk Assessment.Communications in Statistics: Theory and Methods A9: 1749-1874 % McKay, M. D., W. J. Conover and R. J. Beckman. 1979.A Comparison of Three Methods for Selecting Values % of Input Variables in the Analysis of Output from a Computer Code. Technometrics 21: 239-245 % ran=rand(nsample,nvar); s=zeros(nsample,nvar); % method of Stein for j=1: nvar idx=randperm(nsample); P=(idx'-ran(:,j))/nsample; % probability of the cdf s(:,j) = xmean(j) + ltqnorm(P).* xsd(j); % this can be replaced by any inverse distribution function end...
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