Same Joint Distribution, different conditional and marginal distribution I have a group of samples drawn from density function $p(x,y)$, so it has the marginal density $p(x)$ and $p(y)$, and conditional density $p(x|y)$ and $p(y|x)$. 
In what way I can construct another group of samples, with the SAME $p(x,y)$ but the $p(x)$, $p(y)$, $p(x|y)$ and $p(y|x)$ are changed?
 A: The joint density uniquely determines the marginal densities:
$$p(x) = \sum_y p(x,y) ~~\text{or}~~ 
p(x) = \int_{-\infty}^{\infty}p(x,y)\,\mathrm dy$$ 
(similarly for $p(y)$) and so the conditional densities are also
determined uniquely by the joint density.
So the answer is 

No, you cannot construct another group of samples as you desire. 

If the joint density of a population is being estimated from the 
samples instead of being known 
a priori, then different samples from the population will typically give 
slightly different estimates, and so the estimated $p(x,y)$ will be 
slightly different for the two sets of samples, as will marginals etc. 
Again, your desire to have the same joint density and different
marginal densities will not be satisfied.
What you can get is the same marginal densities but different
joint densities. A simple example is two Bernoulli random variables
$X$ and $Y$ each with parameter $\frac{1}{2}$.  


*

*If they are independent, $p(0,0)=p(0,1)=p(1,0)=p(1,1) = \frac{1}{4}$.

*But if $X = 1-Y$, $~p(0,0)=p(1,1)= 0, ~p(1,0)=p(0,1) = \frac{1}{2}$.
Consider also two standard normal random variables. If they are jointly
normal,  then $p(x,y)$ is the bivariate normal density. As a 
special case, if they are independent, then $p(x,y) = p(x)p(y)$.
But they could be marginally normal random variables
that are not jointly normal with joint density
$$p(x,y) = \begin{cases}
2p(x)p(y), & \text{if}~ x \geq 0, y \geq 0, \text{or}~ x < 0, y < 0,\\
0,& \text{otherwise}.\end{cases}$$
Note that the random variables are positively correlated in this
case since the probability mass lies entirely in the first and
third quadrants.  This again illustrates that
the same marginal densities can give rise to different joint densities.
What you want, the same joint density but different marginal densities,
is, alas, not possible. 
