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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= [0.819143,1.55123]
oneSample first= [-0.337688,-0.914279] last= [0.828313,-0.29406]
mean= [0.573157,0.562708]
covariance= [[ 0.7023 7.00129e-05 ]
[ 7.00129e-05 0.67677 ]]
Point= [1.0, 1.0]
log pdf =-1.7971e+00
pdf =1.6578e-01
cdf =5.0107e-01
ccdf =4.9893e-01
survival=8.5345e-02
quantile= [2.234,2.234]
cdf(quantile)= 0.95
InverseSurvival= class=Point name=Unnamed dimension=2 values=[-0.998063,-0.998063]
Survival(inverseSurvival)=0.950000
Unilateral confidence interval (lower tail)= [-7.65063, 2.234]
[-7.65063, 2.234]
beta= [0.974679]
Unilateral confidence interval (upper tail)= [-0.998063, 7.65063]
[-0.998063, 7.65063]
beta= [0.974679]
mean= [0.56419,0.56419]
standard deviation= [0.825645,0.825645]
skewness= [0.136949,0.136949]
kurtosis= [3.06174,3.06174]
covariance= [[ 0.68169 0 ]
[ 0 0.68169 ]]
correlation= [[ 1 0 ]
[ 0 1 ]]
spearman= [[ 1 0 ]
[ 0 1 ]]
kendall= [[ 1 0 ]
[ 0 1 ]]
CumulativeDistributionNetwork([JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 2))), JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 2)))][[0,1],[0,1]])
MarginalDistribution(distribution=CumulativeDistributionNetwork([JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))), JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)))][[0,1,2],[0,1,2]]), indices=[0,1])
CumulativeDistributionNetwork([JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))), JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)))][[0,1,2],[0,1,2]])
CumulativeDistributionNetwork([JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3))), JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), Uniform(a = 0, b = 1), IndependentCopula(dimension = 3)))][[0,1,2],[0,1,2]])
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