Probability Generating functions (I know it's a bit long...):
If X and Y are 2 random variables, the joint probability generating function (pgf), \(G\), of X and Y is defined to be:\[G_{X,Y}(z_1,z_2)=E\left(z_1^Xz_2^Y\right), \text{where } E \text{ is the expected value}\] If X and Y are independent, then \(G_{X,Y}(z_1,z_2)=G_X(z_1)G_Y(z_2) ~~~(**)\) In the univariate case, \(G_X(z)=E(z^X)\) Suppose X and Y are independent random variables, and suppose you have \(W=X+Y\) and \(U=XY\). I feel though by using \((**)\) it looks as if the pgf's of \(T\) and of \(U\) look the same. This can't be, however, since I know the distribution of \(T\) and \(U\) are different and that two different distributions can't have the same pgf. My problem is finding the pgf of \(U\). Let's assume continuous random variables. Let \(W = X+Y\). Showing the pgf of W is standard and found in any textbook dealing with pgfs: \[\begin{align}G_W(z)&=E\left(z^W \right) \\&=E\left(z^{X+Y} \right)\\&=E\left(z^Xz^Y \right)\\&=E\left(z^X \right)E\left(z^Y \right)\text{by independence}\\&=\int\limits_{x\in R_X}z^xf_X(x)\, dx\int\limits_{y \in R_Y}z^yf_Y(y)\,dy,~~ R_X, R_Y \text{ are the support of }X \text{ and }Y\\&=G_X(z)G_Y(z)\end{align} \] I guess here the pgf of W matches \((**)\) as \(z_1=z_2=z\)? Now, I tried doing the same for U: Let \(U=XY\). Then \[\begin{align}G_U(z)&=E\left(z^U \right) \\&=E\left(z^{XY} \right) \\&=G_{XY}(z) \\&=\iint\limits_{(x,y)\in R_{XY}}z^{xy}f_{XY}(x,y)\,dx \,dy, ~~R_{XY}\text{ is the joint support of } X\text{ and } Y \\&=\iint\limits_{(x,y)\in R_X \times R_Y}z^{xy}f_X(x)f_Y(y)\,dx\,dy, ~~\text{by independence} \\&=\int\limits_{x\in R_X}~\int\limits_{y \in R_Y}z^{xy}f_X(x)f_Y(y)\,dy\,dx \\&=\int\limits_{x\in R_X }f_X(x)\int\limits_{y \in R_Y}z^{xy}f_Y(y)\,dy \,dx\end{align} \] How can I match the pgf of U to match \((**)\)
ow, i think i just burned a retina .....
@amistre64 lol
@SithsAndGiggles @kirbykirby @aum
Aren't PGFs only defined for discrete variables?
I don't even know what that is xD
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