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Testing class InverseNormal
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=InverseNormal name=InverseNormal dimension=1 mu=2 lambda=0.5
streamObject(const T & anObject)
class=InverseNormal name=InverseNormal dimension=1 mu=2 lambda=0.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=InverseNormal name=InverseNormal dimension=1 mu=2 lambda=0.5
Distribution InverseNormal(mu = 2, lambda = 0.5)
Elliptical = false
Continuous = true
range=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=[472.248] finite lower bound=[1] finite upper bound=[0]
oneRealization=class=Point name=Unnamed dimension=1 values=[0.632204]
oneSample first=class=Point name=Unnamed dimension=1 values=[6.88993] last=class=Point name=Unnamed dimension=1 values=[0.573857]
mean=class=Point name=Unnamed dimension=1 values=[1.98518]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[14.9406]
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=[3]
log pdf=-2.93426
pdf =0.0531698
pdf (FD)=0.0531698
cdf=0.834308
ccdf=0.165692
survival=0.165692
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.117535]
Survival(inverse survival)=0.95
characteristic function=(0.130777,0.348769)
log characteristic function=(-0.987568,1.21205)
quantile=class=Point name=Unnamed dimension=1 values=[8.26789]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0274495] upper bound=class=Point name=Unnamed dimension=1 values=[8.27076] 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(InverseNormal(mu = 2, lambda = 0.5)) gradientImplementation=MinimumVolumeLevelSetGradient(InverseNormal(mu = 2, lambda = 0.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(InverseNormal(mu = 2, lambda = 0.5)) level=4.73175
beta=0.00881103
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0917281] upper bound=class=Point name=Unnamed dimension=1 values=[12.6739] 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=[8.26789] 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=[0.117535] upper bound=class=Point name=Unnamed dimension=1 values=[472.248] finite lower bound=[1] finite upper bound=[1]
beta=0.95
entropy=1.42375
entropy (MC)=1.42112
mean=class=Point name=Unnamed dimension=1 values=[2]
standard deviation=class=Point name=Unnamed dimension=1 values=[4]
skewness=class=Point name=Unnamed dimension=1 values=[6]
kurtosis=class=Point name=Unnamed dimension=1 values=[63]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[16]
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 : 2, lambda : 0.5]]
Standard representative=InverseNormal(mu = 2, lambda = 0.5)
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