Distribution  class=Exponential name=Exponential dimension=1 lambda=2.5 gamma=-0.5
Distribution  Exponential(lambda = 2.5, gamma = -0.5)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[-0.315107]
oneSample first= class=Point name=Unnamed dimension=1 values=[-0.45014]  last= class=Point name=Unnamed dimension=1 values=[-0.157472]
mean= class=Point name=Unnamed dimension=1 values=[-0.101604]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.160734]
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=[-0.146986]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[-0.146986]
log pdf=-2.833709
pdf     =0.058794
pdf (FD)=0.058794
cdf=0.976482
ccdf=0.023518
characteristic function= (0.921855842528-0.110683201593j)
pdf gradient     = class=Point name=Unnamed dimension=2 values=[-0.0646738,0.146986]
pdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[-0.0646738,0.146986]
cdf gradient     = class=Point name=Unnamed dimension=2 values=[0.0352766,-0.0587944]
cdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[0.0352766,-0.0587944]
quantile= class=Point name=Unnamed dimension=1 values=[0.698293]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.479483]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [-0.5, 0.698293]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.07944} with f=
MinimumVolumeLevelSetEvaluation(Exponential(lambda = 2.5, gamma = -0.5))
beta= [0.125]
Bilateral confidence interval= [-0.489873, 0.975552]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-0.5, 0.698293]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.479483, 12.3945]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[-0.1]
standard deviation= class=Point name=Unnamed dimension=1 values=[0.4]
skewness= class=Point name=Unnamed dimension=1 values=[2]
kurtosis= class=Point name=Unnamed dimension=1 values=[9]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.16]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[lambda,gamma] values=[2.5,-0.5]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [1]
standard moment n= 2  value= [2]
standard moment n= 3  value= [6]
standard moment n= 4  value= [24]
standard moment n= 5  value= [120]
Standard representative= Exponential(lambda = 1, gamma = 0)
