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Testing class SquaredNormal
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=SquaredNormal name=SquaredNormal mu=-1 sigma=1
streamObject(const T & anObject)
class=SquaredNormal name=SquaredNormal mu=-1 sigma=1
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=SquaredNormal name=SquaredNormal mu=5.2 sigma=11.6
Distribution SquaredNormal(mu = 5.2, sigma = 11.6)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[150.188]
oneSample first=class=Point name=Unnamed dimension=1 values=[90.0147] last=class=Point name=Unnamed dimension=1 values=[523.635]
mean=class=Point name=Unnamed dimension=1 values=[160.083]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[50061.1]
skewness=class=Point name=Unnamed dimension=1 values=[2.66931]
kurtosis=class=Point name=Unnamed dimension=1 values=[12.9527]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[9.1]
ddf =class=Point name=Unnamed dimension=1 values=[-0.000581252]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.000581252]
log pdf=-4.60159
pdf =0.0100359
pdf (FD)=0.0100359
cdf=0.185981
ccdf=0.814019
survival=0.814019
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.64685]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=1 values=[617.139]
cdf(quantile)=0.95
quantile (tail)=class=Point name=Unnamed dimension=1 values=[0.64685]
cdf (tail)=0.95
pdf gradient =class=Point name=Unnamed dimension=2 values=[-0.00036172,-0.000656205]
pdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.00036172,-0.000656205]
cdf gradient =class=Point name=Unnamed dimension=2 values=[-0.00702671,-0.012596]
cdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.00702671,-0.012596]
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[617.139] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[X0,-logPDF] evaluationImplementation=MinimumVolumeLevelSetEvaluation(SquaredNormal(mu = 5.2, sigma = 11.6)) gradientImplementation=MinimumVolumeLevelSetGradient(SquaredNormal(mu = 5.2, sigma = 11.6)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(SquaredNormal(mu = 5.2, sigma = 11.6)) level=8.57247
beta=0.000189245
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.161557] upper bound=class=Point name=Unnamed dimension=1 values=[802.599] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[617.139] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.64685] upper bound=class=Point name=Unnamed dimension=1 values=[10774.4] finite lower bound=[1] finite upper bound=[1]
beta=0.95
entropy=5.87051
entropy (MC)=5.87485
mean=class=Point name=Unnamed dimension=1 values=[161.6]
standard deviation=class=Point name=Unnamed dimension=1 values=[225.315]
skewness=class=Point name=Unnamed dimension=1 values=[2.73125]
kurtosis=class=Point name=Unnamed dimension=1 values=[14.0137]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[50766.8]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
parameters=[[mu : 5.2, sigma : 11.6]]
Standard representative=SquaredNormal(mu = 5.2, sigma = 11.6)
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