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algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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
algo result= probabilityEstimate=1.465054e-01 varianceEstimate=2.109726e-04 standard deviation=1.45e-02 coefficient of variation=9.91e-02 confidenceLength(0.95)=5.69e-02 outerSampling=186 blockSize=4
probability distribution= Normal(mu = 0.146505, sigma = 0.0145249)
algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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 maximumStandardDeviation=0.1 blockSize=4
algo result= probabilityEstimate=1.500000e-01 varianceEstimate=8.250000e-03 standard deviation=9.08e-02 coefficient of variation=6.06e-01 confidenceLength(0.95)=3.56e-01 outerSampling=5 blockSize=4
probability distribution= Normal(mu = 0.15, sigma = 0.0908295)
algo= class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence coefficients=[2305843009213693952,2305843009213693952,2305843009213693952,2305843009213693952] seed=1 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] size=4 restart=true randomize=false 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
algo result= probabilityEstimate=1.432292e-01 varianceEstimate=2.038655e-04 standard deviation=1.43e-02 coefficient of variation=9.97e-02 confidenceLength(0.95)=5.60e-02 outerSampling=192 blockSize=4
probability distribution= Normal(mu = 0.143229, sigma = 0.0142781)
algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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 maximumStandardDeviation=0.1 blockSize=4
algo result= probabilityEstimate=6.250000e-02 varianceEstimate=4.638672e-03 standard deviation=6.81e-02 coefficient of variation=1.09e+00 confidenceLength(0.95)=2.67e-01 outerSampling=4 blockSize=4
probability distribution= Normal(mu = 0.0625, sigma = 0.0681078)
algo= class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence coefficients=[2305843009213693952,2305843009213693952,2305843009213693952,2305843009213693952] seed=1 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] size=4 restart=true randomize=true 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
algo result= probabilityEstimate=1.621212e-01 varianceEstimate=2.618680e-04 standard deviation=1.62e-02 coefficient of variation=9.98e-02 confidenceLength(0.95)=6.34e-02 outerSampling=165 blockSize=4
probability distribution= Normal(mu = 0.162121, sigma = 0.0161823)
algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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 maximumStandardDeviation=0.1 blockSize=4
algo result= probabilityEstimate=2.083333e-01 varianceEstimate=8.463542e-03 standard deviation=9.20e-02 coefficient of variation=4.42e-01 confidenceLength(0.95)=3.61e-01 outerSampling=6 blockSize=4
probability distribution= Normal(mu = 0.208333, sigma = 0.0919975)
algo= class=ProbabilitySimulationAlgorithm experiment=class=ImportanceSamplingExperiment name=Unnamed 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] importance distribution=class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[49.969,1.84194,10.4454,4.66776] 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] size=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
algo result= probabilityEstimate=1.533139e-01 varianceEstimate=2.330392e-04 standard deviation=1.53e-02 coefficient of variation=9.96e-02 confidenceLength(0.95)=5.98e-02 outerSampling=43 blockSize=4
probability distribution= Normal(mu = 0.153314, sigma = 0.0152656)
algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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 maximumStandardDeviation=0.1 blockSize=4
algo result= probabilityEstimate=5.000000e-02 varianceEstimate=3.000000e-03 standard deviation=5.48e-02 coefficient of variation=1.10e+00 confidenceLength(0.95)=2.15e-01 outerSampling=5 blockSize=4
probability distribution= Normal(mu = 0.05, sigma = 0.0547723)
algo= class=ProbabilitySimulationAlgorithm experiment=class=LHSExperiment name=Unnamed 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] size=4 alwaysShuffle=true random shift=true 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
algo result= probabilityEstimate=1.360294e-01 varianceEstimate=1.844504e-04 standard deviation=1.36e-02 coefficient of variation=9.98e-02 confidenceLength(0.95)=5.32e-02 outerSampling=204 blockSize=4
probability distribution= Normal(mu = 0.136029, sigma = 0.0135813)
algo= class=ProbabilitySimulationAlgorithm experiment=class=MonteCarloExperiment name=Unnamed 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] size=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 maximumStandardDeviation=0.1 blockSize=4
algo result= probabilityEstimate=1.500000e-01 varianceEstimate=8.250000e-03 standard deviation=9.08e-02 coefficient of variation=6.06e-01 confidenceLength(0.95)=3.56e-01 outerSampling=5 blockSize=4
probability distribution= Normal(mu = 0.15, sigma = 0.0908295)
--------------------------------
composite vector/comparison event
MonteCarlo result= probabilityEstimate=2.473684e-01 varianceEstimate=6.053604e-04 standard deviation=2.46e-02 coefficient of variation=9.95e-02 confidenceLength(0.95)=9.64e-02 outerSampling=95 blockSize=4
probability distribution= Normal(mu = 0.247368, sigma = 0.0246041)
composite vector/domain event
MonteCarlo result= probabilityEstimate=3.333333e-01 varianceEstimate=1.102293e-03 standard deviation=3.32e-02 coefficient of variation=9.96e-02 confidenceLength(0.95)=1.30e-01 outerSampling=63 blockSize=4
probability distribution= Normal(mu = 0.333333, sigma = 0.0332008)
composite vector/interval event
MonteCarlo result= probabilityEstimate=1.927481e-01 varianceEstimate=3.688685e-04 standard deviation=1.92e-02 coefficient of variation=9.96e-02 confidenceLength(0.95)=7.53e-02 outerSampling=131 blockSize=4
probability distribution= Normal(mu = 0.192748, sigma = 0.019206)
process/domain event
MonteCarlo result= probabilityEstimate=9.166667e-01 varianceEstimate=8.101852e-03 standard deviation=9.00e-02 coefficient of variation=9.82e-02 confidenceLength(0.95)=3.53e-01 outerSampling=3 blockSize=4
probability distribution= Normal(mu = 0.916667, sigma = 0.0900103)
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