Distribution  TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = 4)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.9123]
oneSample first= class=Point name=Unnamed dimension=1 values=[1.3426]  last= class=Point name=Unnamed dimension=1 values=[3.87446]
mean= class=Point name=Unnamed dimension=1 values=[2.34616]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.63794]
Point=  class=Point name=Unnamed dimension=1 values=[2.5]
ddf      = class=Point name=Unnamed dimension=1 values=[-0.0851881]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[-0.0851881]
pdf      =0.383346
pdf (ref)=0.383346
cdf=0.576060
ccdf=0.423940
cdf (ref)=0.576060
pdf gradient      = class=Point name=Unnamed dimension=4 values=[0.02466,-0.0590839,0.124394,-0.0638662]
pdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[0.02466,-0.0590839,0.124394,-0.0638662]
cdf gradient      = class=Point name=Unnamed dimension=4 values=[-0.149807,-0.0915298,-0.137567,-0.0959726]
cdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[-0.149807,-0.0915298,-0.137567,-0.0959726]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.14928]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [1, 3.73264]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.57038} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = 4))
beta= [0.207966]
Bilateral confidence interval= [1.07578, 3.85894]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 3.73264]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.14928, 4]
beta= [0.95]
mean      = class=Point name=Unnamed dimension=1 values=[2.35526]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.35526]
standard deviation      = class=Point name=Unnamed dimension=1 values=[0.802475]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[0.802475]
skewness      = class=Point name=Unnamed dimension=1 values=[0.190048]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.190048]
kurtosis      = class=Point name=Unnamed dimension=1 values=[1.9933]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[1.9933]
covariance      = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
parameters      = [class=PointWithDescription name=X0 dimension=6 description=[mean_0,standard_deviation_0,finiteLowerBound,lowerBound,finiteUpperBound,upperBound] values=[2,1.5,1,1,1,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,4]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [-0.0964922]
standard moment n= 2  value= [0.295518]
standard moment n= 3  value= [-0.0546493]
standard moment n= 4  value= [0.168124]
standard moment n= 5  value= [-0.0377976]
Standard representative= TruncatedNormal(mu = -0.333333, sigma = 1, a = -1, b = 1)
Distribution  TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = +inf)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.69199]
oneSample first= class=Point name=Unnamed dimension=1 values=[2.79367]  last= class=Point name=Unnamed dimension=1 values=[1.76978]
mean= class=Point name=Unnamed dimension=1 values=[2.64435]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.23607]
Point=  class=Point name=Unnamed dimension=1 values=[2.5]
ddf      = class=Point name=Unnamed dimension=1 values=[-0.0747934]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[-0.0747934]
pdf      =0.336570
pdf (ref)=0.336570
cdf=0.505769
ccdf=0.494231
cdf (ref)=0.505769
pdf gradient      = class=Point name=Unnamed dimension=3 values=[-0.0210957,-0.135523,0.0958891]
pdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[-0.0210957,-0.135523,0.0958891,0]
cdf gradient      = class=Point name=Unnamed dimension=3 values=[-0.195764,-0.206061,-0.140807]
cdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[-0.195764,-0.206061,-0.140807,0]
quantile= class=Point name=Unnamed dimension=1 values=[4.67299]
quantile= class=Point name=Unnamed dimension=1 values=[4.67299]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.16934]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [1, 4.67299]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.62114} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = +inf))
beta= [0.0727201]
Bilateral confidence interval= [1.08613, 5.12246]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 4.67299]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.16934, 13.4759]
beta= [0.95]
mean      = class=Point name=Unnamed dimension=1 values=[2.64103]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.64103]
standard deviation      = class=Point name=Unnamed dimension=1 values=[1.09456]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[1.09456]
skewness      = class=Point name=Unnamed dimension=1 values=[0.730758]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.730758]
kurtosis      = class=Point name=Unnamed dimension=1 values=[3.23251]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[3.23251]
covariance      = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.19806]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.19806]
parameters      = [class=PointWithDescription name=X0 dimension=6 description=[mean_0,standard_deviation_0,finiteLowerBound,lowerBound,finiteUpperBound,upperBound] values=[2,1.5,1,1,0,13.4759]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,200]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [-0.983507]
standard moment n= 2  value= [0.967408]
standard moment n= 3  value= [-0.951689]
standard moment n= 4  value= [0.936342]
standard moment n= 5  value= [-0.921354]
Standard representative= TruncatedNormal(mu = -0.98995, sigma = 0.0150754, a = -1, b = 1)
Distribution  TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = -inf, b = 4)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.38239]
oneSample first= class=Point name=Unnamed dimension=1 values=[1.09659]  last= class=Point name=Unnamed dimension=1 values=[0.440643]
mean= class=Point name=Unnamed dimension=1 values=[1.72507]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.64216]
Point=  class=Point name=Unnamed dimension=1 values=[2.5]
ddf      = class=Point name=Unnamed dimension=1 values=[-0.0615199]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[-0.0615199]
pdf      =0.276840
pdf (ref)=0.276840
cdf=0.693845
ccdf=0.306155
cdf (ref)=0.693845
pdf gradient      = class=Point name=Unnamed dimension=3 values=[0.0948276,-0.119643,-0.0333077]
pdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[0.0948276,-0.119643,0,-0.0333077]
cdf gradient      = class=Point name=Unnamed dimension=3 values=[-0.19336,0.0190258,-0.0834793]
cdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[-0.19336,0.0190258,0,-0.0834793]
quantile= class=Point name=Unnamed dimension=1 values=[3.64324]
quantile= class=Point name=Unnamed dimension=1 values=[3.64324]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.53617]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [4, 4]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.65813} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = -inf, b = 4))
beta= [0.0700792]
Bilateral confidence interval= [-1.00085, 3.80883]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-9.47594, 3.64324]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.53617, 4]
beta= [0.95]
mean      = class=Point name=Unnamed dimension=1 values=[1.72929]
mean (ref)= class=Point name=Unnamed dimension=1 values=[1.72929]
standard deviation      = class=Point name=Unnamed dimension=1 values=[1.27879]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[1.27879]
skewness      = class=Point name=Unnamed dimension=1 values=[-0.455767]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[-0.455767]
kurtosis      = class=Point name=Unnamed dimension=1 values=[2.84601]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[2.84601]
covariance      = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.6353]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.6353]
parameters      = [class=PointWithDescription name=X0 dimension=6 description=[mean_0,standard_deviation_0,finiteLowerBound,lowerBound,finiteUpperBound,upperBound] values=[2,1.5,0,-9.47594,1,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,-200,4]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [0.977738]
standard moment n= 2  value= [0.956129]
standard moment n= 3  value= [0.93515]
standard moment n= 4  value= [0.91478]
standard moment n= 5  value= [0.894999]
Standard representative= TruncatedNormal(mu = 0.980392, sigma = 0.0147059, a = -1, b = 1)
Distribution  TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = 4)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.19283]
oneSample first= class=Point name=Unnamed dimension=1 values=[1.7907]  last= class=Point name=Unnamed dimension=1 values=[3.44825]
mean= class=Point name=Unnamed dimension=1 values=[2.35742]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643133]
Point=  class=Point name=Unnamed dimension=1 values=[2.5]
ddf      = class=Point name=Unnamed dimension=1 values=[-0.0851881]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[-0.0851881]
pdf      =0.383346
pdf (ref)=0.383346
cdf=0.576060
ccdf=0.423940
cdf (ref)=0.576060
pdf gradient      = class=Point name=Unnamed dimension=4 values=[0.02466,-0.0590839,0.124394,-0.0638662]
pdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[0.02466,-0.0590839,0.124394,-0.0638662]
cdf gradient      = class=Point name=Unnamed dimension=4 values=[-0.149807,-0.0915298,-0.137567,-0.0959726]
cdf gradient (ref)= class=Point name=Unnamed dimension=4 values=[-0.149807,-0.0915298,-0.137567,-0.0959726]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.14928]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [1, 3.73264]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.57038} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), a = 1, b = 4))
beta= [0.207966]
Bilateral confidence interval= [1.07578, 3.85894]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 3.73264]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.14928, 4]
beta= [0.95]
mean      = class=Point name=Unnamed dimension=1 values=[2.35526]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.35526]
standard deviation      = class=Point name=Unnamed dimension=1 values=[0.802475]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[0.802475]
skewness      = class=Point name=Unnamed dimension=1 values=[0.190048]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.190048]
kurtosis      = class=Point name=Unnamed dimension=1 values=[1.9933]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[1.9933]
covariance      = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
parameters      = [class=PointWithDescription name=X0 dimension=6 description=[mean_0,standard_deviation_0,finiteLowerBound,lowerBound,finiteUpperBound,upperBound] values=[2,1.5,1,1,1,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,4]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [-0.0964922]
standard moment n= 2  value= [0.295518]
standard moment n= 3  value= [-0.0546493]
standard moment n= 4  value= [0.168124]
standard moment n= 5  value= [-0.0377976]
Standard representative= TruncatedNormal(mu = -0.333333, sigma = 1, a = -1, b = 1)
