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Distribution class=Mixture name=Mixture dimension=3 distributionCollection=[class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[0.5,-0.5,1] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1],class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[1.5,0.5,2] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1],class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[2.5,1.5,3] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1]] weightsDistribution=class=UserDefined name=UserDefined dimension=1 collection=[(class=NumericalPoint name=Unnamed dimension=1 values=[0],0.333333),(class=NumericalPoint name=Unnamed dimension=1 values=[1],0.333333),(class=NumericalPoint name=Unnamed dimension=1 values=[2],0.333333)]
Weights = class=NumericalPoint name=Unnamed dimension=3 values=[0.333333,0.333333,0.333333]
After update, new weights = class=NumericalPoint name=Unnamed dimension=3 values=[0.5,0.25,0.25]
Distribution class=Mixture name=Mixture dimension=3 distributionCollection=[class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[0.5,-0.5,1] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1],class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[1.5,0.5,2] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1],class=Normal name=Normal dimension=3 mean=class=NumericalPoint name=Unnamed dimension=3 values=[2.5,1.5,3] sigma=class=NumericalPoint name=Unnamed dimension=3 values=[2,3,1] correlationMatrix=class=CorrelationMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,0.5,0,0.5,1,0.5,0,0.5,1]] weightsDistribution=class=UserDefined name=UserDefined dimension=1 collection=[(class=NumericalPoint name=Unnamed dimension=1 values=[0],0.333333),(class=NumericalPoint name=Unnamed dimension=1 values=[1],0.333333),(class=NumericalPoint name=Unnamed dimension=1 values=[2],0.333333)]
Elliptical = False
Continuous = True
oneRealization= class=NumericalPoint name=Unnamed dimension=3 values=[2.07328,-1.03125,2.18976]
oneSample first= class=NumericalPoint name=Unnamed dimension=3 values=[-1.86277,-0.862642,2.91223] last= class=NumericalPoint name=Unnamed dimension=3 values=[1.56533,-1.10765,2.76013]
mean= class=NumericalPoint name=Unnamed dimension=3 values=[1.64038,0.699193,2.03941]
covariance= class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[4.88439,3.99293,0.644831,3.99293,9.41608,2.09091,0.644831,2.09091,1.58061]
Point= class=NumericalPoint name=Unnamed dimension=3 values=[1,1,1]
ddf = class=NumericalPoint name=Unnamed dimension=3 values=[0.00119456,-0.00197254,0.00476249]
ddf (FD)= class=NumericalPoint name=Unnamed dimension=3 values=[0.00119456,-0.00197254,0.00476249]
log pdf=-5.135163
pdf =0.005886
cdf=0.115110
ccdf=0.884890
quantile= class=NumericalPoint name=Unnamed dimension=3 values=[5.96551,6.93727,4.61725]
cdf(quantile)=0.950000
mean= class=NumericalPoint name=Unnamed dimension=3 values=[1.5,0.5,2]
covariance= class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[4.66667,3.66667,0.666667,3.66667,9.66667,2.16667,0.666667,2.16667,1.66667]
parameters= [class=NumericalPointWithDescription name=Normal dimension=2 description=[mean_0,standard_deviation_0] values=[0.5,2],class=NumericalPointWithDescription name=Normal dimension=2 description=[mean_0,standard_deviation_0] values=[1.5,2],class=NumericalPointWithDescription name=Normal dimension=2 description=[mean_0,standard_deviation_0] values=[2.5,2],class=NumericalPointWithDescription name=dependence dimension=9 description=[atom_0_R_1_0,atom_0_R_2_0,atom_0_R_2_1,atom_1_R_1_0,atom_1_R_2_0,atom_1_R_2_1,atom_2_R_1_0,atom_2_R_2_0,atom_2_R_2_1] values=[0.5,0,0.5,0.5,0,0.5,0.5,0,0.5]]
standard moment n= 0 value= [1,1,1]
standard moment n= 1 value= [1.5,0.5,2]
standard moment n= 2 value= [6.91667,9.91667,5.66667]
standard moment n= 3 value= [24.375,14.625,18]
standard moment n= 4 value= [132.729,294.229,63.6667]
standard moment n= 5 value= [650.094,711.281,242]
Standard representative= Mixture((w = 0.333333, d = Normal(mu = [0.5,-0.5,1], sigma = [2,3,1], R = [[ 1 0.5 0 ]
[ 0.5 1 0.5 ]
[ 0 0.5 1 ]])), (w = 0.333333, d = Normal(mu = [1.5,0.5,2], sigma = [2,3,1], R = [[ 1 0.5 0 ]
[ 0.5 1 0.5 ]
[ 0 0.5 1 ]])), (w = 0.333333, d = Normal(mu = [2.5,1.5,3], sigma = [2,3,1], R = [[ 1 0.5 0 ]
[ 0.5 1 0.5 ]
[ 0 0.5 1 ]])))
newMixture pdf= 0.135652911626
atoms kept in mixture= [Normal(mu = 2, sigma = 2),Normal(mu = 3, sigma = 3)]
newMixture= Mixture((w = 0.0724638, d = Normal(mu = 2, sigma = 2)), (w = 0.927536, d = Normal(mu = 3, sigma = 3)))
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