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Distribution class=Wishart name=Wishart dimension=1 cholesky=class=TriangularMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] nu=3
Distribution Wishart(V =
[[ 1 ]], nu = 3)
Mean= class=Point name=Unnamed dimension=1 values=[3]
Covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[6]
Elliptical = False
oneRealization= class=Point name=Unnamed dimension=1 values=[3.90923]
Point= class=Point name=Unnamed dimension=1 values=[9.1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.00565984]
log pdf=-4.364801
pdf =0.012717
cdf=0.972010
ccdf=0.027990
pdf gradient = class=Point name=Unnamed dimension=2 values=[0.0387874,0.00940205]
cdf gradient = class=Point name=Unnamed dimension=2 values=[-0.115726,-0.0234707]
quantile= class=Point name=Unnamed dimension=1 values=[7.81473]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.351846]
Survival(inverseSurvival)=0.950000
entropy=2.054120
Minimum volume interval= [0.00315933, 7.81683]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 3.79922} with f=
MinimumVolumeLevelSetEvaluation(Wishart(V =
[[ 1 ]], nu = 3))
beta= [0.0223883]
Bilateral confidence interval= [0.215795, 9.3484]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0, 7.81473]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.351846, 59.8961]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[3]
standard deviation= class=Point name=Unnamed dimension=1 values=[2.44949]
skewness= class=Point name=Unnamed dimension=1 values=[1.63299]
kurtosis= class=Point name=Unnamed dimension=1 values=[7]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[6]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[V_0_0,nu] values=[1,3]]
Standard representative= Wishart(V =
[[ 1 ]], nu = 3)
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