Distribution class=InverseWishart name=InverseWishart dimension=1 cholesky=class=TriangularMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] nu=5
Distribution InverseWishart(V = 
[[ 1 ]], nu = 5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.156697]
oneSample first=class=Point name=Unnamed dimension=1 values=[0.374649] last=class=Point name=Unnamed dimension=1 values=[0.381059]
mean=class=Point name=Unnamed dimension=1 values=[0.335077]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.249466]
skewness=class=Point name=Unnamed dimension=1 values=[25.4956]
kurtosis=class=Point name=Unnamed dimension=1 values=[1165.62]
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=[9.1]
ddf     =class=Point name=Unnamed dimension=1 values=[-2.09622e-05]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-2.09622e-05]
log pdf=-9.80146
pdf     =5.53709e-05
pdf (FD)=5.53709e-05
cdf=0.999795
ccdf=0.000204753
survival=0.000204753
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.0903302]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[0.872999]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[0.0903302]
cdf (tail)=0.95
pdf gradient     =class=Point name=Unnamed dimension=2 values=[0.000135385,-9.97944e-05]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.000503875,0.000410437]
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0458714] upper bound=class=Point name=Unnamed dimension=1 values=[0.877774] 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,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(InverseWishart(V = 
[[ 1 ]], nu = 5)) gradientImplementation=MinimumVolumeLevelSetGradient(InverseWishart(V = 
[[ 1 ]], nu = 5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(InverseWishart(V = 
[[ 1 ]], nu = 5)) level=16.35
beta=7.92992e-08
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0779271] upper bound=class=Point name=Unnamed dimension=1 values=[1.20306] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[0.872999] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0903302] upper bound=class=Point name=Unnamed dimension=1 values=[59.8961] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.333333]
standard deviation=class=Point name=Unnamed dimension=1 values=[1]
skewness=class=Point name=Unnamed dimension=1 values=[18.5998]
kurtosis=class=Point name=Unnamed dimension=1 values=[1074.6]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
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]
parameters=[[nu : 1,  : 5]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[0.333143]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[0.299063]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[1.74217]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[40.1732]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[1456.57]
Standard representative=InverseWishart(V = 
[[ 1 ]], nu = 5)
