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algo= class=ExpectationSimulationAlgorithm randomVector=class=RandomVector implementation=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,y0,y1] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0,y1] formulas=[-F*L^3/(3*E*I),-F*L^4/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0,y1] formulas=[-F*L^3/(3*E*I),-F*L^4/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0,y1] formulas=[-F*L^3/(3*E*I),-F*L^4/(3*E*I)] antecedent=class=UsualRandomVector distribution=class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[50,1,10,5] sigma=class=Point name=Unnamed dimension=4 values=[1,1,1,1] correlationMatrix=class=CorrelationMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1] maximumOuterSampling=250000 coefficientOfVariationCriterionType=NONE maximumCoefficientOfVariation=0.1 standardDeviationCriterionType=MAX maximumStandardDeviation=0 maximumStandardDeviationPerComponent=class=Point name=Unnamed dimension=0 values=[] blockSize=2
result= expectationEstimate=class=Point name=Unnamed dimension=2 values=[-1.44292,-14.8534] varianceEstimate=class=Point name=Unnamed dimension=2 values=[5.36933e-06,0.00063331] outerSampling=250000 blockSize=2
mu= [-1.43624,-14.784] var= [2.67527,315.266]
Normal(mu = [-1.44292,-14.8534], sigma = [0.00231718,0.0251656], R = [[ 1 0 ]
[ 0 1 ]])
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