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Distribution TruncatedNormal(mu = 1.5, sigma = 3, a = -2, b = 5)
Elliptical = True
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[3.3246]
Point= class=Point name=Unnamed dimension=1 values=[3.5]
ddf = class=Point name=Unnamed dimension=1 values=[-0.0312729]
log pdf=-1.960925
pdf =0.140728
cdf=0.827107
ccdf=0.172893
pdf gradient = class=Point name=Unnamed dimension=4 values=[0.0312729,0.0031597,0.0125231,-0.0125231]
log-pdf gradient = class=Point name=Unnamed dimension=4 values=[0.222222,0.0224525,0.0889876,-0.0889876]
cdf gradient = class=Point name=Unnamed dimension=4 values=[-0.0517406,-0.0258989,-0.0153853,-0.0736023]
quantile= class=Point name=Unnamed dimension=1 values=[4.48948]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-1.48948]
Survival(inverseSurvival)=0.950000
entropy=1.927246
Minimum volume interval= [-1.73303, 4.73303]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.3194} with f=
MinimumVolumeLevelSetEvaluation(TruncatedNormal(mu = 1.5, sigma = 3, a = -2, b = 5))
beta= [0.0983328]
Bilateral confidence interval= [-1.73303, 4.73303]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-2, 4.48948]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-1.48948, 5]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[1.5]
standard deviation= class=Point name=Unnamed dimension=1 values=[1.84222]
skewness= class=Point name=Unnamed dimension=1 values=[0]
kurtosis= class=Point name=Unnamed dimension=1 values=[1.99309]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[3.39378]
parameters= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[1.5,3,-2,5]]
Standard representative= TruncatedNormal(mu = 0, sigma = 0.857143, a = -1, b = 1)
Distribution TruncatedNormal(mu = 50, sigma = 1, a = 3, b = 4)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[3.96641]
Point= class=Point name=Unnamed dimension=1 values=[3.5]
ddf = class=Point name=Unnamed dimension=1 values=[1.93801e-07]
log pdf=-19.295887
pdf =0.000000
cdf=0.000000
ccdf=1.000000
pdf gradient = class=Point name=Unnamed dimension=4 values=[-1.99336e-09,1.84427e-07,0,-1.91808e-07]
log-pdf gradient = class=Point name=Unnamed dimension=4 values=[-0.478281,44.2509,0,-46.0217]
cdf gradient = class=Point name=Unnamed dimension=4 values=[-4.47731e-11,4.14344e-09,-2.95456e-19,-4.12299e-09]
quantile= class=Point name=Unnamed dimension=1 values=[3.99889]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[3.93495]
Survival(inverseSurvival)=0.950000
entropy=-2.829585
Minimum volume interval= [3.93495, 4]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= -0.834793} with f=
MinimumVolumeLevelSetEvaluation(TruncatedNormal(mu = 50, sigma = 1, a = 3, b = 4))
beta= [2.30434]
Bilateral confidence interval= [3.91991, 3.99945]
beta= [0.95]
Unilateral confidence interval (lower tail)= [3, 3.99889]
beta= [0.95]
Unilateral confidence interval (upper tail)= [3.93495, 4]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[3.97828]
standard deviation= class=Point name=Unnamed dimension=1 values=[0.0217084]
skewness= class=Point name=Unnamed dimension=1 values=[-1.99718]
kurtosis= class=Point name=Unnamed dimension=1 values=[-1.99718]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.000471255]
parameters= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[50,1,3,4]]
Standard representative= TruncatedNormal(mu = 93, sigma = 2, a = -1, b = 1)
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