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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]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.146986]
log pdf=-2.833709
pdf =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]
cdf gradient = 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
entropy=0.083709
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 representative= Gamma(k = 1, lambda = 1, gamma = 0)
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