Distribution  Triangular(a = -0.5, m = 1.5, b = 2.5)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.44403]
oneSample first= class=Point name=Unnamed dimension=1 values=[1.90705]  last= class=Point name=Unnamed dimension=1 values=[1.09635]
mean= class=Point name=Unnamed dimension=1 values=[1.17256]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.39567]
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=[0.333333]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[0.333333]
log pdf=-0.693147
pdf     =0.500000
pdf (FD)=0.500000
cdf=0.375000
ccdf=0.625000
characteristic function= (0.312305424736+0.758322070069j)
pdf gradient     = class=Point name=Unnamed dimension=3 values=[0.0833333,-0.25,-0.166667]
pdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[0.0833333,-0.25,-0.166667]
cdf gradient     = class=Point name=Unnamed dimension=3 values=[-0.1875,-0.1875,-0.5]
cdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[-0.1875,-0.1875,-0.125]
quantile= class=Point name=Unnamed dimension=1 values=[0.724745]
cdf(quantile)=0.250000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.0477226]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [-0.0527864, 2.27639]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.90333} with f=
MinimumVolumeLevelSetEvaluation(Triangular(a = -0.5, m = 1.5, b = 2.5))
beta= [0.149071]
Bilateral confidence interval= [-0.112702, 2.22614]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-0.5, 2.1127]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.0477226, 2.5]
beta= [0.95]
Point=  class=Point name=Unnamed dimension=1 values=[2]
ddf     = class=Point name=Unnamed dimension=1 values=[-0.666667]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[-0.666667]
log pdf=-1.098612
pdf     =0.333333
pdf (FD)=0.333333
cdf=0.916667
ccdf=0.083333
pdf gradient     = class=Point name=Unnamed dimension=3 values=[0.111111,0.333333,0.222222]
pdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[0.111111,0.333333,0.222222]
cdf gradient     = class=Point name=Unnamed dimension=3 values=[-0.0277778,-0.0833333,-0.222222]
cdf gradient (FD)= class=Point name=Unnamed dimension=3 values=[-0.0277778,-0.0833333,-0.222222]
quantile= class=Point name=Unnamed dimension=1 values=[2.1127]
cdf(quantile)=0.950000
mean= class=Point name=Unnamed dimension=1 values=[1.16667]
standard deviation= class=Point name=Unnamed dimension=1 values=[0.62361]
skewness= class=Point name=Unnamed dimension=1 values=[-0.305441]
kurtosis= class=Point name=Unnamed dimension=1 values=[2.4]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.388889]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[a,m,b] values=[-0.5,1.5,2.5]]
