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Distribution JointByConditioningDistribution(Y, X with X|Theta~Normal(Theta), Theta=f(Y), f=id_2, Y~JointDistribution(Uniform(a = 0, b = 1), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2)))
Parameters [[a_X0 : 0, b_X0 : 1, a_X1 : 1, b_X1 : 2]]
Mean [0.5,1.5,0.5]
Elliptical distribution= False
Elliptical copula= False
Independent copula= False
oneRealization= [0.629877,1.88281,-1.75408]
oneSample= [ Y0 Y1 X0 ]
0 : [ 0.347057 1.96942 2.72115 ]
1 : [ 0.0632061 1.29276 0.515725 ]
2 : [ 0.373767 1.73727 2.87065 ]
3 : [ 0.92851 1.82081 2.3727 ]
4 : [ 0.359802 1.95475 0.870026 ]
5 : [ 0.085785 1.66073 -2.04474 ]
6 : [ 0.0245595 1.41892 -0.104255 ]
7 : [ 0.66134 1.48977 0.453587 ]
8 : [ 0.345319 1.65598 1.08304 ]
9 : [ 0.646334 1.06421 1.12074 ]
anotherSample mean= [0.498071,1.49953,0.504279]
anotherSample covariance= [[ 0.0830803 0.000313403 0.0767514 ]
[ 0.000313403 0.0834798 -0.000108455 ]
[ 0.0767514 -0.000108455 2.37804 ]]
Point point= [0.5, 1.5, 1.0] pdf=0.251589 cdf=0.181637
Quantile= [0.982796,1.9828,3.87457]
CDF(quantile)= 0.95
conditional PDF=1.09340e-01
conditional CDF=9.08789e-01
conditional quantile=2.50000e+00
sequential conditional PDF= [1,1,0.10934]
sequential conditional CDF( [0.5, 1.5, 2.5] )= [0.5,0.5,0.908789]
sequential conditional quantile( [0.5,0.5,0.908789] )= [0.5,1.5,2.5]
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