Testing class CumulativeDistributionNetwork
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=CumulativeDistributionNetwork name=CumulativeDistributionNetwork dimension=1 distributionCollection=[class=Uniform name=Uniform dimension=1 a=-1 b=1] graph=[[0]]
streamObject(const T & anObject)
class=CumulativeDistributionNetwork name=CumulativeDistributionNetwork dimension=1 distributionCollection=[class=Uniform name=Uniform dimension=1 a=-1 b=1] graph=[[0]]
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=CumulativeDistributionNetwork name=CumulativeDistributionNetwork dimension=2 distributionCollection=[class=Normal name=Normal dimension=2 mean=class=Point name=Unnamed dimension=2 values=[0,0] sigma=class=Point name=Unnamed dimension=2 values=[1,1] correlationMatrix=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1],class=Normal name=Normal dimension=2 mean=class=Point name=Unnamed dimension=2 values=[0,0] sigma=class=Point name=Unnamed dimension=2 values=[1,1] correlationMatrix=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1]] graph=[[0,1],[0,1]]
Distribution CumulativeDistributionNetwork([Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]])), Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]]))][[0,1],[0,1]])
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=2 values=[0.819143,1.55123]
oneSample first=class=Point name=Unnamed dimension=2 values=[-0.337688,0.500922] last=class=Point name=Unnamed dimension=2 values=[-0.31133,-0.29406]
mean=class=Point name=Unnamed dimension=2 values=[0.57312,0.562745]
covariance=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.693077,-0.0044764,-0.0044764,0.685993]
Point= class=Point name=Unnamed dimension=2 values=[1,1]
log pdf=-1.7971
pdf     =0.16578
pdf (FD)=0.16578
cdf=0.50107
ccdf=0.49893
survival=0.085345
Inverse survival=class=Point name=Unnamed dimension=2 values=[-0.99806,-0.99806]
Survival(inverse survival)=0.95
quantile=class=Point name=Unnamed dimension=2 values=[2.234,2.234]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=2 lower bound=class=Point name=Unnamed dimension=2 values=[-7.6506,-7.6506] upper bound=class=Point name=Unnamed dimension=2 values=[7.6506,7.6506] finite lower bound=[1,1] finite upper bound=[1,1]
threshold=1
Minimum volume level set=class=LevelSet name=Unnamed dimension=2 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,x1,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(CumulativeDistributionNetwork([Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]])), Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]]))][[0,1],[0,1]])) gradientImplementation=MinimumVolumeLevelSetGradient(CumulativeDistributionNetwork([Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]])), Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]]))][[0,1],[0,1]])) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=2 values=[0.0001,0.0001] evaluation=MinimumVolumeLevelSetEvaluation(CumulativeDistributionNetwork([Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]])), Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]]))][[0,1],[0,1]])) level=4.473
beta=0.011413
Bilateral confidence interval=class=Interval name=Unnamed dimension=2 lower bound=class=Point name=Unnamed dimension=2 values=[-7.6506,-7.6506] upper bound=class=Point name=Unnamed dimension=2 values=[7.6506,7.6506] finite lower bound=[1,1] finite upper bound=[1,1]
beta=1
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=2 lower bound=class=Point name=Unnamed dimension=2 values=[-7.6506,-7.6506] upper bound=class=Point name=Unnamed dimension=2 values=[2.234,2.234] finite lower bound=[1,1] finite upper bound=[1,1]
beta=0.97468
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=2 lower bound=class=Point name=Unnamed dimension=2 values=[-0.99806,-0.99806] upper bound=class=Point name=Unnamed dimension=2 values=[7.6506,7.6506] finite lower bound=[1,1] finite upper bound=[1,1]
beta=0.97468
mean=class=Point name=Unnamed dimension=2 values=[0.56419,0.56419]
standard deviation=class=Point name=Unnamed dimension=2 values=[0.82565,0.82565]
skewness=class=Point name=Unnamed dimension=2 values=[0.13695,0.13695]
kurtosis=class=Point name=Unnamed dimension=2 values=[3.0617,3.0617]
covariance=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.68169,0,0,0.68169]
correlation=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1]
spearman=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1]
kendall=class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1]
