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If you have two random variables that are independent say $X\sim f_X (vars)$ and $Y \sim f_Y (vars)$.

Is this a way to produce a bivariate distribution $f_{(X,Y)}$?

$f_{(X,Y)} = p(X=x \cap Y=y) = p(x) \times p(y) = f_X(x)\times f_Y(y)$.

Does this hold for the case of integrals as well in the continuous case?

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Yes, the joint cumulative distribution function $F_{X,Y}(x,y)$, that is, the probability that $X\le x$ and $Y\le y$, is the product of the individual cdf $F_X(x)$ and $F_Y(y)$.

If $X$ and $Y$ have continuous distributions with density functions $f_X(x)$ and $f_Y(y)$, then the joint density function $f_{X,Y}(x,y)$ is the product of the individual density functions.

The discrete case is as you described it.

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  • $\begingroup$ I assume it works for the pmf as well, which is what I was implying vaguely above $\endgroup$
    – AER
    Jul 3, 2014 at 0:57
  • $\begingroup$ I have added a couple of lines about the pmf (discrete case) and pdf (continuous case). $\endgroup$ Jul 3, 2014 at 1:00

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