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Distribution class=Student name=Student dimension=1 nu=6.5 mean=class=Point name=Unnamed dimension=1 values=[-0.5] sigma=class=Point name=Unnamed dimension=1 values=[2] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
Distribution Student(nu = 6.5, mu = -0.5, sigma = 2)
Elliptical = True
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.46561]
oneSample first= class=Point name=Unnamed dimension=1 values=[3.53872] last= class=Point name=Unnamed dimension=1 values=[-0.636935]
mean= class=Point name=Unnamed dimension=1 values=[-0.517373]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[5.598]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.0560023]
log pdf=-1.961638
pdf =0.140628
cdf=0.760243
ccdf=0.239757
Distribution class=Student name=Student dimension=2 nu=7.5 mean=class=Point name=Unnamed dimension=2 values=[0,1] sigma=class=Point name=Unnamed dimension=2 values=[0.5,1] correlationMatrix=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0.25,0.25,1]
Distribution Student(nu = 7.5, mu = [0,1], sigma = [0.5,1], R = [[ 1 0.25 ]
[ 0.25 1 ]])
Elliptical = True
Continuous = True
oneRealization= class=Point name=Unnamed dimension=2 values=[-0.215452,1.21185]
oneSample first= class=Point name=Unnamed dimension=2 values=[0.954969,1.04649] last= class=Point name=Unnamed dimension=2 values=[0.749137,4.3505]
mean= class=Point name=Unnamed dimension=2 values=[-0.00643012,0.974389]
covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.339995,0.166276,0.166276,1.40222]
Point= class=Point name=Unnamed dimension=2 values=[1,1]
ddf = class=Point name=Unnamed dimension=2 values=[-0.13334,0.0166675]
log pdf=-3.251707
pdf =0.038708
cdf=0.487148
ccdf=0.512852
quantile= class=Point name=Unnamed dimension=2 values=[1.14225,3.28449]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=2 values=[-1.14225,-1.28449]
Survival(inverseSurvival)=0.950000
entropy=2.379127
Minimum volume interval= [-1.37255, 1.37255]
[-1.7451, 3.7451]
threshold= [0.973154]
Minimum volume level set= {x | f(x) <= 4.90705} with f=
MinimumVolumeLevelSetEvaluation(Student(nu = 7.5, mu = [0,1], sigma = [0.5,1], R = [[ 1 0.25 ]
[ 0.25 1 ]]))
beta= [0.00739423]
Bilateral confidence interval= [-1.37255, 1.37255]
[-1.7451, 3.7451]
beta= [0.973154]
Unilateral confidence interval (lower tail)= [-77.5591, 1.14225]
[-154.118, 3.28449]
beta= [0.973087]
Unilateral confidence interval (upper tail)= [-1.14225, 77.5591]
[-1.28449, 156.118]
beta= [0.973087]
mean= class=Point name=Unnamed dimension=2 values=[0,1]
standard deviation= class=Point name=Unnamed dimension=2 values=[0.583874,1.16775]
skewness= class=Point name=Unnamed dimension=2 values=[0,0]
kurtosis= class=Point name=Unnamed dimension=2 values=[4.71429,4.71429]
covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.340909,0.170455,0.170455,1.36364]
parameters= [class=PointWithDescription name=Unnamed dimension=3 description=[nu,mu_0,sigma_0] values=[7.5,0,0.5],class=PointWithDescription name=Unnamed dimension=3 description=[nu,mu_1,sigma_1] values=[7.5,1,1],class=PointWithDescription name=Unnamed dimension=2 description=[nu,R_1_0] values=[7.5,0.25]]
Standard representative= Student(nu = 7.5, mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]])
density generator=0.051781
pdf via density generator=0.038708
density generator derivative =-0.025891
density generator derivative (FD)=-0.025891
density generator second derivative =0.015671
density generator second derivative (FD)=0.015671
conditional PDF=0.348531
conditional CDF=0.285577
conditional quantile=1.381698
sequential conditional PDF= [0.125473,0.277346]
sequential conditional CDF( [1.5,2.5] )= [0.990805,0.623728]
sequential conditional quantile( [0.990805,0.623728] )= [1.5,2.5]
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