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distribution= ConditionalDistribution(X with X|Theta~ComposedDistribution(Theta), Theta=f(Y), f=[y0,y1,y2,y3]->[y0,y1,y2,y3], Y~ComposedDistribution(Dirac(point = [1]), Dirac(point = [2]), Bernoulli(p = 0.7), Uniform(a = 3, b = 4), IndependentCopula(dimension = 4)))
Parameters [[point_0_marginal_0 : 1],[point_0_marginal_1 : 2],[p_marginal_2 : 0.7],[a_marginal_3 : 3, b_marginal_3 : 4],[]]
Mean [1.5,2.1]
Covariance [[ 0.0833333 0 ]
[ 0 0.751111 ]]
Elliptical distribution= False
Elliptical copula= False
Independent copula= False
oneRealization= [1.13528,1.0937]
oneSample= [ marginal 1 marginal 2 ]
0 : [ 1.92068 2.49374 ]
1 : [ 1.71438 1.87896 ]
2 : [ 1.8835 1.80748 ]
3 : [ 1.68457 3.16374 ]
4 : [ 1.58862 1.53788 ]
5 : [ 1.21044 2.02765 ]
6 : [ 1.98184 3.21605 ]
7 : [ 1.25986 2.2968 ]
8 : [ 1.11108 1.85243 ]
9 : [ 1.97898 2.729 ]
anotherSample mean= [1.50538,2.09375]
anotherSample covariance= [[ 0.0847776 -0.000929088 ]
[ -0.000929088 0.74965 ]]
Zero point= [0,0] pdf= 0.0 cdf= 0.0
Quantile= [1.97468,3.60341]
CDF(quantile)= 0.95
conditioning distribution= ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2))
Distribution ConditionalDistribution(X with X|Theta~Normal(Theta), Theta=f(Y), f=[y0,y1]->[y0,y1], Y~ComposedDistribution(Uniform(a = 0, b = 1), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2)))
Parameters [[a_marginal_0 : 0, b_marginal_0 : 1],[a_marginal_1 : 1, b_marginal_1 : 2],[]]
Mean [0.5]
Covariance [[ 2.41667 ]]
Elliptical distribution= False
Elliptical copula= True
Independent copula= True
oneRealization= [-0.21251]
oneSample= [ marginal 1 ]
0 : [ 1.04021 ]
1 : [ -0.768799 ]
2 : [ -0.0327311 ]
3 : [ 1.95381 ]
4 : [ 1.43698 ]
5 : [ 1.31895 ]
6 : [ 2.05226 ]
7 : [ -1.44045 ]
8 : [ 2.03224 ]
9 : [ 0.258808 ]
anotherSample mean= [0.503649]
anotherSample covariance= [[ 2.41134 ]]
Zero point= [0] pdf=0.253748 cdf=0.367604
Quantile= [3.05046]
CDF(quantile)= 0.95
conditioning distribution= ComposedDistribution(Binomial(n = 3, p = 0.5), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2))
Distribution ConditionalDistribution(X with X|Theta~Normal(Theta), Theta=f(Y), f=[y0,y1]->[y0,y1], Y~ComposedDistribution(Binomial(n = 3, p = 0.5), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2)))
Parameters [[n_marginal_0 : 3, p_marginal_0 : 0.5],[a_marginal_1 : 1, b_marginal_1 : 2],[]]
Mean [1.5]
Covariance [[ 3.08333 ]]
Elliptical distribution= False
Elliptical copula= True
Independent copula= True
oneRealization= [2.22062]
oneSample= [ marginal 1 ]
0 : [ 0.106685 ]
1 : [ 1.52532 ]
2 : [ 3.2931 ]
3 : [ 4.70326 ]
4 : [ 2.99546 ]
5 : [ 5.81408 ]
6 : [ 2.47839 ]
7 : [ 3.09905 ]
8 : [ 4.94457 ]
9 : [ -0.310612 ]
anotherSample mean= [1.49593]
anotherSample covariance= [[ 3.16541 ]]
Zero point= [0] pdf=0.156744 cdf=0.192205
Quantile= [4.38019]
CDF(quantile)= 0.95
conditioning distribution= ComposedDistribution(Dirac(point = [0.5]), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2))
Distribution ConditionalDistribution(X with X|Theta~Normal(Theta), Theta=f(Y), f=[y0,y1]->[y0,y1], Y~ComposedDistribution(Dirac(point = [0.5]), Uniform(a = 1, b = 2), IndependentCopula(dimension = 2)))
Parameters [[point_0_marginal_0 : 0.5],[a_marginal_1 : 1, b_marginal_1 : 2],[]]
Mean [0.5]
Covariance [[ 2.33333 ]]
Elliptical distribution= False
Elliptical copula= True
Independent copula= True
oneRealization= [-0.917771]
oneSample= [ marginal 1 ]
0 : [ 2.27217 ]
1 : [ -0.858827 ]
2 : [ -0.344631 ]
3 : [ 0.480758 ]
4 : [ 3.19028 ]
5 : [ 0.813164 ]
6 : [ 1.29442 ]
7 : [ -1.95841 ]
8 : [ -1.39477 ]
9 : [ 0.861998 ]
anotherSample mean= [0.528452]
anotherSample covariance= [[ 2.34096 ]]
Zero point= [0] pdf=0.258535 cdf=0.364782
Quantile= [3.00635]
CDF(quantile)= 0.95
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