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Distribution class=Logistic name=Logistic dimension=1 mu=-0.5 beta=1.5
Distribution Logistic(mu = -0.5, beta = 1.5)
Elliptical = True
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.297531]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.0403812]
log pdf=-2.031988
pdf =0.131075
cdf=0.731059
ccdf=0.268941
characteristic function= (0.0743069833187-0.0405940899998j)
pdf gradient = class=Point name=Unnamed dimension=2 values=[0.0403812,-0.0470019]
cdf gradient = class=Point name=Unnamed dimension=2 values=[-0.131075,-0.131075]
quantile= class=Point name=Unnamed dimension=1 values=[3.91666]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-4.91666]
Survival(inverseSurvival)=0.950000
entropy=2.405465
Minimum volume interval= [-5.99534, 4.99534]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 4.11966} with f=
MinimumVolumeLevelSetEvaluation(Logistic(mu = -0.5, beta = 1.5))
beta= [0.01625]
Bilateral confidence interval= [-5.99534, 4.99534]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-48.8543, 3.91666]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-4.91666, 47.8543]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[-0.5]
standard deviation= class=Point name=Unnamed dimension=1 values=[2.7207]
skewness= class=Point name=Unnamed dimension=1 values=[0]
kurtosis= class=Point name=Unnamed dimension=1 values=[4.2]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[7.4022]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[mu,beta] values=[-0.5,1.5]]
Standard representative= Logistic(mu = 0, beta = 1)
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