Marginal Probability Mass Functions

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twoski
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Homework Statement



Discrete random variables X and Y , whose values are positive integers, have the joint
probability mass function pXY(x,y) = 2-x-y. Determine the marginal probability mass
functions pX(x) and pY(y). Are X and Y independent? Determine E[X], E[Y], and E[XY].

Homework Equations



Independence is determined by whether p(x,y) = p(x)p(y) for all x and y.

The Attempt at a Solution



My notes don't have much on the topic of determining marginal PMF's using a JPMF... I was hoping someone could point me in the right direction.
 
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twoski said:

Homework Statement



Discrete random variables X and Y , whose values are positive integers, have the joint
probability mass function pXY(x,y) = 2-x-y. Determine the marginal probability mass
functions pX(x) and pY(y). Are X and Y independent? Determine E[X], E[Y], and E[XY].

Homework Equations



Independence is determined by whether p(x,y) = p(x)p(y) for all x and y.

The Attempt at a Solution



My notes don't have much on the topic of determining marginal PMF's using a JPMF... I was hoping someone could point me in the right direction.

The marginal pmf of X is
[tex]p_X(x) = P\{ X = x, Y \leq \infty\} = \sum_{\text{all }y} p(x,y).[/tex]
BTW: it is bad form to use the same symbol p to stand for three different things in the same problem. Instead, use subscripts, like this (##p_X(x), p_Y(y)##) or different letters, like this: ##g(x)## and ##h(y)##.
 
Ray Vickson said:
The marginal pmf of X is
[tex]p_X(x) = P\{ X = x, Y \leq \infty\} = \sum_{\text{all }y} p(x,y).[/tex]
BTW: it is bad form to use the same symbol p to stand for three different things in the same problem. Instead, use subscripts, like this (##p_X(x), p_Y(y)##) or different letters, like this: ##g(x)## and ##h(y)##.

So if that's the marginal PMF of x, then for y...

[tex]p_Y(y) = P\{ X \leq \infty\ , Y = y} = \sum_{\text{all }x} p(x,y).[/tex]
 
twoski said:
So if that's the marginal PMF of x, then for y...

[tex]p_Y(y) = P\{ X \leq \infty\ , Y = y\} = \sum_{\text{all }x} p_{X,Y}(x,y).[/tex]

Fixed it, and yes that is correct.
 
So using these 2 PMFs i have to determine whether X and Y are independent. Going by the definition i'd say they are independent.

Is this right?

[tex]E[X] = \sum_{\text{k}} x_k * p_X(x_k) = \sum_{\text{k}} x_k * \sum_{\text{x}} p(x,y).[/tex]
 
Your ##E(X)## is right. For the next question, use the theorem: If ##X,Y## are independent, then ##E(XY) = E(X)E(Y)##.
 
twoski said:
So using these 2 PMFs i have to determine whether X and Y are independent. Going by the definition i'd say they are independent.

Is this right?

[tex]E[X] = \sum_{\text{k}} x_k * p_X(x_k) = \sum_{\text{k}} x_k * \sum_{\text{x}} p(x,y).[/tex]

Your equation makes no sense: it is essentially summing over x twice, and not doing anything with y.