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Testing class Multinomial
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=Multinomial name=Multinomial dimension=3 p=class=Point name=Unnamed dimension=3 values=[0.25,0.25,0.25] n=5
streamObject(const T & anObject)
class=Multinomial name=Multinomial dimension=3 p=class=Point name=Unnamed dimension=3 values=[0.25,0.25,0.25] n=5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=Multinomial name=Multinomial dimension=3 p=class=Point name=Unnamed dimension=3 values=[0.25,0.25,0.25] n=5
Distribution Multinomial(n = 5, p = [0.25,0.25,0.25])
Elliptical = false
Continuous = false
oneRealization=class=Point name=Unnamed dimension=3 values=[1,2,0]
oneSample first=class=Point name=Unnamed dimension=3 values=[0,1,4] last=class=Point name=Unnamed dimension=3 values=[1,3,1]
mean=class=Point name=Unnamed dimension=3 values=[1.2578,1.2409,1.2607]
covariance=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.941633,-0.305935,-0.321041,-0.305935,0.941561,-0.317234,-0.321041,-0.317234,0.956031]
support=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=56 dimension=3 data=[[0,0,0],[1,0,0],[0,1,0],[0,0,1],[2,0,0],[1,1,0],[1,0,1],[0,2,0],[0,1,1],[0,0,2],[3,0,0],[2,1,0],[2,0,1],[1,2,0],[1,1,1],[1,0,2],[0,3,0],[0,2,1],[0,1,2],[0,0,3],[4,0,0],[3,1,0],[3,0,1],[2,2,0],[2,1,1],[2,0,2],[1,3,0],[1,2,1],[1,1,2],[1,0,3],[0,4,0],[0,3,1],[0,2,2],[0,1,3],[0,0,4],[5,0,0],[4,1,0],[4,0,1],[3,2,0],[3,1,1],[3,0,2],[2,3,0],[2,2,1],[2,1,2],[2,0,3],[1,4,0],[1,3,1],[1,2,2],[1,1,3],[1,0,4],[0,5,0],[0,4,1],[0,3,2],[0,2,3],[0,1,4],[0,0,5]]
support restricted to the interval=class=Interval name=Unnamed dimension=3 lower bound=class=Point name=Unnamed dimension=3 values=[1,1,1] upper bound=class=Point name=Unnamed dimension=3 values=[3,3,3] finite lower bound=[1,1,1] finite upper bound=[1,1,1] gives=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=10 dimension=3 data=[[1,1,1],[2,1,1],[1,2,1],[1,1,2],[3,1,1],[2,2,1],[2,1,2],[1,3,1],[1,2,2],[1,1,3]]
Point= class=Point name=Unnamed dimension=3 values=[1,1,1]
log pdf([1,1,1])=-2.83713
pdf ([1,1,1])=0.0585937
cdf ([1,1,1])=0.13281
ccdf ([1,1,1])=0.86719
survival([1,1,1])=0.380859
quantile(0.95)=class=Point name=Unnamed dimension=3 values=[3,3,3]
cdf(quantile)=0.953125
probability([0, 1]
[1, 2]
[2, 3])=0.195313
entropy=3.70986
entropy (MC)=3.70971
mean=class=Point name=Unnamed dimension=3 values=[1.25,1.25,1.25]
covariance=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[0.9375,-0.3125,-0.3125,-0.3125,0.9375,-0.3125,-0.3125,-0.3125,0.9375]
correlation=class=CovarianceMatrix dimension=3 implementation=class=MatrixImplementation name=Unnamed rows=3 columns=3 values=[1,-0.333333,-0.333333,-0.333333,1,-0.333333,-0.333333,-0.333333,1]
parameters=[[n : 5, p_0 : 0.25],[n : 5, p_1 : 0.25],[n : 5, p_2 : 0.25],[n : 5, p_0 : 0.25, p_1 : 0.25, p_2 : 0.25]]
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