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Distribution class=WeibullMin name=WeibullMin dimension=1 beta=2 alpha=1.5 gamma=-0.5
Distribution WeibullMin(beta = 2, alpha = 1.5, gamma = -0.5)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.49188]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.107263]
log pdf=-1.081042
pdf =0.339242
cdf=0.477703
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.0891733,0.191956,0.107263]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.254431,-0.0975938,-0.339242]
quantile= class=Point name=Unnamed dimension=1 values=[3.65622]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.223897]
Survival(inverseSurvival)=0.950000
entropy=1.480087
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.92424} with f=
MinimumVolumeLevelSetEvaluation(WeibullMin(beta = 2, alpha = 1.5, gamma = -0.5))
beta= [0.0537054]
Bilateral confidence interval= [-0.327556, 4.27485]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-0.5, 3.65622]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.223897, 19.7578]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[1.30549]
standard deviation= class=Point name=Unnamed dimension=1 values=[1.22587]
skewness= class=Point name=Unnamed dimension=1 values=[1.07199]
kurtosis= class=Point name=Unnamed dimension=1 values=[4.3904]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.50276]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[beta,alpha,gamma] values=[2,1.5,-0.5]]
Standard representative= WeibullMin(beta = 1, alpha = 1.5, gamma = 0)
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