Distribution  class=LogNormal name=LogNormal dimension=1 muLog=-1 sigmaLog=1.5 gamma=-0.5
Distribution  LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.416038]
oneSample first= class=Point name=Unnamed dimension=1 values=[-0.444936]  last= class=Point name=Unnamed dimension=1 values=[3.12047]
mean= class=Point name=Unnamed dimension=1 values=[0.609446]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[8.23002]
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=[-0.12381]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[-0.12381]
log pdf=-2.168831
pdf     =0.114311
pdf (FD)=0.114311
cdf=0.825615
ccdf=0.174385
characteristic function= (0.74087680493-0.0157786395662j)
pdf gradient     = class=Point name=Unnamed dimension=3 values=[0.0714046,-0.00930299,0.12381]
pdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[0.0714046,-0.00930299,0.12381]
cdf gradient     = class=Point name=Unnamed dimension=3 values=[-0.171467,-0.16066,-0.114311]
cdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[-0.171467,-0.16066,-0.114311]
quantile= class=Point name=Unnamed dimension=1 values=[3.83742]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.468798]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [-0.499653, 3.83753]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 4.14451} with f=
MinimumVolumeLevelSetEvaluation(LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5))
beta= [0.0158512]
Bilateral confidence interval= [-0.480551, 6.45838]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-0.5, 3.83742]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.468798, 35451.8]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[0.633148]
standard deviation= class=Point name=Unnamed dimension=1 values=[3.30128]
skewness= class=Point name=Unnamed dimension=1 values=[33.468]
kurtosis= class=Point name=Unnamed dimension=1 values=[10078.3]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[10.8985]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[muLog,sigmaLog,gamma] values=[-1,1.5,-0.5]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [1.13315]
standard moment n= 2  value= [12.1825]
standard moment n= 3  value= [1242.65]
standard moment n= 4  value= [1.2026e+06]
standard moment n= 5  value= [1.10423e+10]
Standard representative= LogNormal(muLog = -1, sigmaLog = 1.5, gamma = 0)
