Distribution class=MarginalDistribution name=MarginalDistribution dimension=3 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] indices=[2,0,1]
Distribution MarginalDistribution(distribution=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 ]]), indices=[2,0,1])
Elliptical = true
Continuous = true
oneRealization=class=Point name=Unnamed dimension=3 values=[-0.438266,0.608202,-1.26617]
oneSample first=class=Point name=Unnamed dimension=3 values=[1.43725,0.350042,-0.355007] last=class=Point name=Unnamed dimension=3 values=[0.859992,-2.2281,0.884963]
mean=class=Point name=Unnamed dimension=3 values=[-0.000164216,0.00955544,0.000744822]
covariance=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1.00735,0.0155386,-0.00400052,0.0155386,1.02164,-0.0214975,-0.00400052,-0.0214975,0.997907]
Point= class=Point name=Unnamed dimension=3 values=[1,1,1]
ddf     =class=Point name=Unnamed dimension=3 values=[-8.56912e-29,-8.56912e-29,-8.56912e-29]
log pdf=-64.6268
pdf     =8.56912e-29
cdf=0.595555
ccdf=0.404445
survival=3.99359e-31
quantile=class=Point name=Unnamed dimension=3 values=[2.1212,2.1212,2.1212]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=3 values=[-0.336086,-0.336086,-0.336086]
cdf (tail)=0.95
pdf gradient     =class=Point name=Unnamed dimension=10 values=[8.56912e-29,8.56912e-29,8.56912e-29,6.55591e-28,6.55591e-28,0,0,0,4.92999e-27,4.92999e-27]
cdf gradient     =class=Point name=Unnamed dimension=10 values=[0,0,0,0,0,0,0,0,0,0]
mean=class=Point name=Unnamed dimension=3 values=[0,0,0]
standard deviation=class=Point name=Unnamed dimension=3 values=[1,1,1]
skewness=class=Point name=Unnamed dimension=3 values=[0,0,0]
kurtosis=class=Point name=Unnamed dimension=3 values=[3,3,3]
covariance=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0,0,0,1,0,0,0,1]
correlation=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0,0,0,1,0,0,0,1]
spearman=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0,0,0,1,0,0,0,1]
kendall=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0,0,0,1,0,0,0,1]
standard moment n=0, value=class=Point name=Unnamed dimension=3 values=[1,1,1]
standard moment n=1, value=class=Point name=Unnamed dimension=3 values=[-7.93531e-68,-7.90057e-68,-7.90057e-68]
standard moment n=2, value=class=Point name=Unnamed dimension=3 values=[3.65843e-53,3.65843e-53,3.65843e-53]
standard moment n=3, value=class=Point name=Unnamed dimension=3 values=[1.19003e-67,1.23586e-67,1.23586e-67]
standard moment n=4, value=class=Point name=Unnamed dimension=3 values=[1.09753e-52,1.09753e-52,1.09753e-52]
standard moment n=5, value=class=Point name=Unnamed dimension=3 values=[1.77784e-67,1.87415e-67,1.87415e-67]
Standard representative=MarginalDistribution(distribution=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 ]]), indices=[2,0,1])
