Testing class TruncatedNormal
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=TruncatedNormal name=TruncatedNormal dimension=1 mu=0.5 sigma=3 a=-2 b=2
streamObject(const T & anObject)
class=TruncatedNormal name=TruncatedNormal dimension=1 mu=0.5 sigma=3 a=-2 b=2
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=TruncatedNormal name=TruncatedNormal dimension=1 mu=0.5 sigma=3 a=-2 b=2
Distribution TruncatedNormal(mu = 0.5, sigma = 3, a = -2, b = 2)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[-0.814797]
oneSample first=class=Point name=Unnamed dimension=1 values=[1.55013] last=class=Point name=Unnamed dimension=1 values=[-0.505664]
mean=class=Point name=Unnamed dimension=1 values=[0.0635542]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.25177]
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=[1]
ddf     =class=Point name=Unnamed dimension=1 values=[-0.0148955]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.0148955]
log pdf=-1.31632
pdf     =0.26812
pdf (FD)=0.26812
cdf=0.743877
ccdf=0.256123
survival=0.256123
Inverse survival=class=Point name=Unnamed dimension=1 values=[-1.74843]
Survival(inverse survival)=0.95
characteristic function=(0.482702,0.0461049)
log characteristic function=(-0.723814,0.0952254)
pdf gradient     =class=Point name=Unnamed dimension=4 values=[0.0277138,-0.0118013,0.0515102,-0.0643285]
pdf gradient (FD)=class=Point name=Unnamed dimension=4 values=[0.0277138,-0.0118013,0.0515102,-0.0643285]
log-pdf gradient     =class=Point name=Unnamed dimension=4 values=[0.103363,-0.0440151,0.192116,-0.239924]
log-pdf gradient (FD)=class=Point name=Unnamed dimension=4 values=[0.103363,-0.0440151,0.192116,-0.239924]
cdf gradient     =class=Point name=Unnamed dimension=4 values=[-0.0404404,0.00354582,-0.0492055,-0.178474]
cdf gradient (FD)=class=Point name=Unnamed dimension=4 values=[-0.0404404,0.00354582,-0.0492055,-0.178474]
quantile=class=Point name=Unnamed dimension=1 values=[1.79498]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-1.74843] upper bound=class=Point name=Unnamed dimension=1 values=[2] 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,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(TruncatedNormal(mu = 0.5, sigma = 3, a = -2, b = 2)) gradientImplementation=MinimumVolumeLevelSetGradient(TruncatedNormal(mu = 0.5, sigma = 3, a = -2, b = 2)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(TruncatedNormal(mu = 0.5, sigma = 3, a = -2, b = 2)) level=1.58329
beta=0.205299
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-1.87213] upper bound=class=Point name=Unnamed dimension=1 values=[1.89667] 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=[-2] upper bound=class=Point name=Unnamed dimension=1 values=[1.79498] 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=[-1.74843] upper bound=class=Point name=Unnamed dimension=1 values=[2] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.0697287]
standard deviation=class=Point name=Unnamed dimension=1 values=[1.1195]
skewness=class=Point name=Unnamed dimension=1 values=[-0.0708515]
kurtosis=class=Point name=Unnamed dimension=1 values=[1.86887]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.25327]
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 : 0.5, sigma : 3, a : -2, b : 2]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[0.0348644]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[0.314534]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[0.0203874]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[0.184018]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[0.0143554]
Standard representative=TruncatedNormal(mu = 0.25, sigma = 1.5, a = -1, b = 1)
