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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
Confidence length at level 99%=0.0748273
Confidence length at level 80%=0.0372288
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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
algo=class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence derived from class=LowDiscrepancySequenceImplementation dimension=4 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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
Confidence length at level 99%=0.0735562
Confidence length at level 80%=0.0365964
algo=class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence derived from class=LowDiscrepancySequenceImplementation dimension=4 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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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=8.333333e-02 varianceEstimate=8.101852e-03 standard deviation=9.00e-02 coefficient of variation=1.08e+00 confidenceLength(0.95)=3.53e-01 outerSampling=3 blockSize=4
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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
Confidence length at level 99%=0.0786433
Confidence length at level 80%=0.0391274
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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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=8.305132e-02 varianceEstimate=6.897522e-03 standard deviation=8.31e-02 coefficient of variation=1.00e+00 confidenceLength(0.95)=3.26e-01 outerSampling=1 blockSize=4
algo=class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence derived from class=LowDiscrepancySequenceImplementation dimension=4 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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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=1.511299e-01 varianceEstimate=2.275829e-04 standard deviation=1.51e-02 coefficient of variation=9.98e-02 confidenceLength(0.95)=5.91e-02 outerSampling=177 blockSize=4
Confidence length at level 99%=0.0777171
Confidence length at level 80%=0.0386666
algo=class=ProbabilitySimulationAlgorithm experiment=class=LowDiscrepancyExperiment name=Unnamed sequence=class=LowDiscrepancySequence implementation=class=SobolSequence derived from class=LowDiscrepancySequenceImplementation dimension=4 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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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=8.333333e-02 varianceEstimate=8.101852e-03 standard deviation=9.00e-02 coefficient of variation=1.08e+00 confidenceLength(0.95)=3.53e-01 outerSampling=3 blockSize=4
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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
Confidence length at level 99%=0.069966
Confidence length at level 80%=0.0348101
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=Simulation event=class=Event name=Unnamed implementation=class=EventRandomVector antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[E,F,L,I,d] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] formulas=[-F*L^3/(3*E*I)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[E,F,L,I] outputVariablesNames=[d] 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
