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)
Mean=  class=Point name=Unnamed dimension=1 values=[0.333333]
Covariance=  class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Elliptical =  False
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]
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=-9.801456
pdf     =0.000055
pdf (FD)=0.000055
cdf=0.999795
ccdf=0.000205
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]
quantile= class=Point name=Unnamed dimension=1 values=[0.872999]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.0903302]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [0.0458714, 0.877774]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 16.35} with f=
MinimumVolumeLevelSetEvaluation(InverseWishart(V = 
[[ 1 ]], nu = 5))
beta= [7.92992e-08]
Bilateral confidence interval= [0.0779271, 1.20306]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0, 0.872999]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.0903302, 59.8961]
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]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[nu,] values=[1,5]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [0.333143]
standard moment n= 2  value= [0.299063]
standard moment n= 3  value= [1.74217]
standard moment n= 4  value= [40.1732]
standard moment n= 5  value= [1456.57]
Standard representative= InverseWishart(V = 
[[ 1 ]], nu = 5)
Run test_computeLogPDF... SUCCESS
Run test_computeLogPDF_1D_case... SUCCESS
Run test_computeLogPDF_diagonal_case... SUCCESS
Run test_getSample_getMean... SUCCESS
