uqlab
MOpts.mString = '1-((0.00064.*X(:,1))./(X(:,2)))-((0.000016.*(X(:,3).^2))./(X(:,2).^2))';
MOpts.isVectorized = 1;
myModel = uq_createModel(MOpts);
iOpts.Marginals(1).Type = 'Gaussian';
iOpts.Marginals(1).Moments = [2000 400]; 
iOpts.Marginals(2).Type = 'Lognormal';
iOpts.Marginals(2).Moments = [5 0.5];
iOpts.Marginals(3).Type = 'Gaussian';
iOpts.Marginals(3).Moments = [500 100];
myInput=uq_createInput(iOpts);
ALROpts.Type = 'Reliability';
ALROpts.Method = 'ALR';
%X=uq_getSample(50,'LHS');
%G=uq_evalModel(X);
%ALROpts.ALR.IExpDesign.X = X;
%ALROpts.ALR.IExpDesign.G = G;
ALROpts.ALR.IExpDesign.N = 50;
ALROpts.ALR.IExpDesign.Sampling = 'LHS';
ALROpts.ALR.MaxAddedED = 300;
ALROpts.ALR.Reliability='MCS';
ALROpts.ALR.Metamodel='PCK';
PCKOpts.EstimMethod='ML';
PCKOpts.Mode='Optimal';
PCKOpts.PCE.Degree=1:20;
PCKOpts.Kriging.Optim.Method='HGA';
ALROpts.ALR.PCK = PCKOpts ;
ALROpts.ALR.LearningFunction='CMM';
ALROpts.ALR.Convergence='StopPfStab';
myALRAnalysis = uq_createAnalysis(ALROpts);
uq_print(myALRAnalysis)
uq_display(myALRAnalysis)

