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DirectionalSampling= class=DirectionalSampling rootStrategy=class=RootStrategy implementation=class=SafeAndSlow derived from class=RootStrategyImplementation solver=class=Solver implementation=class=Brent derived from class=SolverImplementation absoluteError=1e-05 relativeError=1e-05 residualError=0 maximumCallsNumber=100 callsNumber=0 maximumDistance=8 stepSize=1 samplingStrategy=class=SamplingStrategy implementation=class=RandomDirection derived from class=SamplingStrategyImplementation dimension=4 derived from class=EventSimulation event=class=RandomVector implementation=class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(3.*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(3.*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(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] operator=class=Less name=Unnamed threshold=-3 maximumOuterSampling=250 maximumCoefficientOfVariation=0.1 maximumStandardDeviation=0 blockSize=4
DirectionalSampling result= probabilityEstimate=1.748120e-01 varianceEstimate=2.978217e-04 standard deviation=1.73e-02 coefficient of variation=9.87e-02 confidenceLength(0.95)=6.76e-02 outerSampling=30 blockSize=4
DirectionalSampling= class=DirectionalSampling rootStrategy=class=RootStrategy implementation=class=MediumSafe derived from class=RootStrategyImplementation solver=class=Solver implementation=class=Brent derived from class=SolverImplementation absoluteError=1e-05 relativeError=1e-05 residualError=0 maximumCallsNumber=100 callsNumber=0 maximumDistance=8 stepSize=1 samplingStrategy=class=SamplingStrategy implementation=class=OrthogonalDirection derived from class=SamplingStrategyImplementation dimension=4 size=1 derived from class=EventSimulation event=class=RandomVector implementation=class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(3.*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(3.*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[y0] formulas=[-F*L^3/(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] operator=class=Less name=Unnamed threshold=-3 maximumOuterSampling=250 maximumCoefficientOfVariation=0.1 maximumStandardDeviation=0 blockSize=4
DirectionalSampling result= probabilityEstimate=1.459782e-01 varianceEstimate=3.520996e-05 standard deviation=5.93e-03 coefficient of variation=4.06e-02 confidenceLength(0.95)=2.33e-02 outerSampling=1 blockSize=4
X: Uniform(a = -2, b = 1.99999)
p=0.5 (ncalls = 35)
X: Uniform(a = -2, b = 2)
p=0.5 (ncalls = 35)
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