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Distribution class=SmoothedUniform name=SmoothedUniform dimension=1 a=-0.5 b=1.5 sigma=0.5
Distribution SmoothedUniform(a = -0.5, b = 1.5, sigma = 0.5)
Mean= class=Point name=Unnamed dimension=1 values=[0.5]
Covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.583333]
Elliptical = True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.15307]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.237539]
log pdf=-0.867507
pdf =0.419997
cdf=0.729267
ccdf=0.270733
characteristic function= (0.65168889443+0.356019265631j)
pdf gradient = class=Point name=Unnamed dimension=3 values=[0.205567,0.031972,-0.255266]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.134692,-0.285306,-0.118769]
quantile= class=Point name=Unnamed dimension=1 values=[1.74644]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.746443]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [-0.951173, 1.95117]
threshold= 0.95
Minimum volume level set= {x | f(x) <= 2.38884} with f=
MinimumVolumeLevelSetEvaluation(SmoothedUniform(a = -0.5, b = 1.5, sigma = 0.5))
beta=0.091736
Bilateral confidence interval= [-0.951173, 1.95117]
beta=0.95
Unilateral confidence interval (lower tail)= [-4.32531, 1.74644]
beta=0.95
Unilateral confidence interval (upper tail)= [-0.746443, 5.32531]
beta=0.95
mean= class=Point name=Unnamed dimension=1 values=[0.5]
standard deviation= class=Point name=Unnamed dimension=1 values=[0.763763]
skewness= class=Point name=Unnamed dimension=1 values=[0]
kurtosis= class=Point name=Unnamed dimension=1 values=[2.60816]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.583333]
parameters= [class=PointWithDescription name=SmoothedUniform dimension=3 description=[a,b,sigma] values=[-0.5,1.5,0.5]]
Standard representative= SmoothedUniform(a = -1, b = 1, sigma = 1)
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