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Distribution class=MaximumDistribution name=MaximumDistribution distribution=class=Normal name=Normal dimension=5 mean=class=Point name=Unnamed dimension=5 values=[0,0,0,0,0] sigma=class=Point name=Unnamed dimension=5 values=[1,1,1,1,1] correlationMatrix=class=CorrelationMatrix dimension=5 implementation=class=MatrixImplementation name=Unnamed rows=5 columns=5 values=[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1]
Distribution MaximumDistribution(Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]))
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[1.20548]
oneSample first=class=Point name=Unnamed dimension=1 values=[1.43725] last=class=Point name=Unnamed dimension=1 values=[0.884963]
mean=class=Point name=Unnamed dimension=1 values=[1.16745]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.448379]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf =class=Point name=Unnamed dimension=1 values=[0.0911751]
log pdf=-0.500516
pdf =0.606218
cdf=0.42157
ccdf=0.57843
survival=0.57843
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.123843]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[2.31868]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[0.123843]
cdf (tail)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.112778] upper bound=class=Point name=Unnamed dimension=1 values=[2.50221] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[X0,-logPDF] evaluationImplementation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]))) gradientImplementation=MinimumVolumeLevelSetGradient(MaximumDistribution(Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]))) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]))) level=2.46478
beta=0.0850272
beta=0.95
beta=0.95
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[1.16296]
standard deviation=class=Point name=Unnamed dimension=1 values=[0.66898]
skewness=class=Point name=Unnamed dimension=1 values=[0.302571]
kurtosis=class=Point name=Unnamed dimension=1 values=[3.20079]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.447534]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Standard representative=MaximumDistribution(Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]))
Distribution class=MaximumDistribution name=MaximumDistribution distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Distribution MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.813356]
oneSample first=class=Point name=Unnamed dimension=1 values=[0.764471] last=class=Point name=Unnamed dimension=1 values=[1.04927]
mean=class=Point name=Unnamed dimension=1 values=[1.16838]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.449251]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf =class=Point name=Unnamed dimension=1 values=[0.0911751]
log pdf=-0.500516
pdf =0.606218
cdf=0.42157
ccdf=0.57843
survival=0.57843
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.123843]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[2.31868]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[0.123843]
cdf (tail)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.112778] upper bound=class=Point name=Unnamed dimension=1 values=[2.50221] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[X0,-logPDF] evaluationImplementation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) gradientImplementation=MinimumVolumeLevelSetGradient(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) level=2.46478
beta=0.0850272
beta=0.95
beta=0.95
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[1.16296]
standard deviation=class=Point name=Unnamed dimension=1 values=[0.66898]
skewness=class=Point name=Unnamed dimension=1 values=[0.302571]
kurtosis=class=Point name=Unnamed dimension=1 values=[3.20079]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.447534]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Standard representative=MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))
Distribution class=MaximumDistribution name=MaximumDistribution distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Distribution MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.813356]
oneSample first=class=Point name=Unnamed dimension=1 values=[0.764471] last=class=Point name=Unnamed dimension=1 values=[1.04927]
mean=class=Point name=Unnamed dimension=1 values=[1.16838]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.449251]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf =class=Point name=Unnamed dimension=1 values=[0.0911751]
log pdf=-0.500516
pdf =0.606218
cdf=0.42157
ccdf=0.57843
survival=0.57843
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.123843]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[2.31868]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[0.123843]
cdf (tail)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.112778] upper bound=class=Point name=Unnamed dimension=1 values=[2.50221] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[X0,-logPDF] evaluationImplementation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) gradientImplementation=MinimumVolumeLevelSetGradient(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))) level=2.46478
beta=0.0850272
beta=0.95
beta=0.95
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[1.16296]
standard deviation=class=Point name=Unnamed dimension=1 values=[0.66898]
skewness=class=Point name=Unnamed dimension=1 values=[0.302571]
kurtosis=class=Point name=Unnamed dimension=1 values=[3.20079]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.447534]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Standard representative=MaximumDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 5)))
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