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Test indices accessors True
Test distribution accessors True
Distribution class=MarginalDistribution name=MarginalDistribution dimension=3 distribution=class=Normal name=Normal dimension=5 mean=class=Point name=Unnamed dimension=5 values=[0,0,0,0,0] sigma=class=Point name=Unnamed dimension=5 values=[1,1,1,1,1] correlationMatrix=class=CorrelationMatrix dimension=5 implementation=class=MatrixImplementation name=Unnamed rows=5 columns=5 values=[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1] indices=[2,0,1]
Distribution MarginalDistribution(distribution=Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]), indices=[2,0,1])
Elliptical = True
Continuous = True
Discrete = False
Integral = False
oneRealization= [-0.438266,0.608202,-1.26617]
oneSample first= [1.43725,0.350042,-0.355007] last= [0.859992,-2.2281,0.884963]
mean= [-0.000164216,0.00955544,0.000744822]
covariance= [[ 1.00735 0.0155386 -0.00400052 ]
[ 0.0155386 1.02164 -0.0214975 ]
[ -0.00400052 -0.0214975 0.997907 ]]
Point= [1.0, 1.0, 1.0]
ddf = [-0.0141673,-0.0141673,-0.0141673]
log pdf=-4.25682e+00
pdf =1.41673e-02
cdf =5.95555e-01
ccdf =4.04445e-01
survival=3.99359e-03
Inverse survival= [-2.1212,-2.1212,-2.1212]
Survival(inverse survival)=9.50000e-01
quantile= [2.1212,2.1212,2.1212]
cdf(quantile)=9.50000e-01
quantile (tail)= [-0.336086,-0.336086,-0.336086]
cdf (tail)=9.50000e-01
mean= [0,0,0]
standard deviation= [1,1,1]
skewness= [0,0,0]
kurtosis= [3,3,3]
covariance= [[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]]
correlation= [[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]]
spearman= [[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]]
kendall= [[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]]
Standard representative= MarginalDistribution(distribution=Normal(mu = [0,0,0,0,0], sigma = [1,1,1,1,1], R = 5x5
[[ 1 0 0 0 0 ]
[ 0 1 0 0 0 ]
[ 0 0 1 0 0 ]
[ 0 0 0 1 0 ]
[ 0 0 0 0 1 ]]), indices=[2,0,1])
Test indices accessors True
Test distribution accessors True
Distribution class=MarginalDistribution name=MarginalDistribution dimension=3 distribution=class=Multinomial name=Multinomial dimension=5 p=class=Point name=Unnamed dimension=5 values=[0.142857,0.142857,0.142857,0.142857,0.142857] n=10 indices=[2,0,1]
Distribution MarginalDistribution(distribution=Multinomial(n = 10, p = [0.142857,0.142857,0.142857,0.142857,0.142857]), indices=[2,0,1])
Elliptical = False
Continuous = False
Discrete = True
Integral = True
oneRealization= [1,1,2]
oneSample first= [3,2,3] last= [2,1,2]
mean= [1.4117,1.444,1.4221]
covariance= [[ 1.22113 -0.212816 -0.220401 ]
[ -0.212816 1.25399 -0.210933 ]
[ -0.220401 -0.210933 1.20305 ]]
Point= [1.0, 1.0, 1.0]
pdf =2.50271e-03
cdf =1.35956e-01
ccdf =8.64044e-01
survival=4.57828e-01
quantile= [4,4,4]
cdf(quantile)=9.76008e-01
quantile (tail)= [1,1,1]
cdf (tail)=9.96288e-01
mean= [1.42857,1.42857,1.42857]
standard deviation= [1.10657,1.10657,1.10657]
skewness= [0.645497,0.645497,0.645497]
kurtosis= [3.21667,3.21667,3.21667]
covariance= [[ 1.22449 -0.204082 -0.204082 ]
[ -0.204082 1.22449 -0.204082 ]
[ -0.204082 -0.204082 1.22449 ]]
correlation= [[ 1 -0.166667 -0.166667 ]
[ -0.166667 1 -0.166667 ]
[ -0.166667 -0.166667 1 ]]
Standard representative= MarginalDistribution(distribution=Multinomial(n = 10, p = [0.142857,0.142857,0.142857,0.142857,0.142857]), indices=[2,0,1])
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