Finding Marginal Probability Mass Functions for Random Variables X and Y

In summary, the joint probability mass function (p.m.f) of the random variables X and Y is given in the table. To find the marginal p.m.f's P_X \left( x \right) and P_Y \left( y \right), we use the formula P_X \left( x \right) = \sum\limits_{all\;y} {P_{X,Y} \left( {x,y} \right)} and P_Y \left( y \right) = \sum\limits_{all\;x} {P_{X,Y} \left( {x,y} \right)}, where we sum over all possible values of x and y. The marginal p.m.f of X
  • #1
steven10137
118
0

Homework Statement


The following table gives the joint probability mass function (p.m.f) of the random variables X and Y.

http://img170.imageshack.us/img170/555/tableph9.jpg

Find the marginal p.m.f's [tex]P_X \left( x \right)[/tex] and [tex]P_Y \left( y \right)[/tex]

2. The attempt at a solution

I think I have just missed the point of this somewhere.
I know that:
[tex]{P_X \left( x \right) = \sum\limits_{all\;y} {P_{X,Y} \left( {x,y} \right)} }[/tex]
and
[tex]{P_Y \left( y \right) = \sum\limits_{all\;x} {P_{X,Y} \left( {x,y} \right)} }[/tex]

I just don't know how to apply this to the question properly.

For [tex]P_X \left( x \right)[/tex] it's the sum of [tex]{P_{X,Y} \left( {x,y} \right)}[/tex] over all y (y=0,1,2). So do we just take the first row?
i.e. 0.15+0.20+0.10 = 0.45?

Following this, would
[tex]P_Y \left( y \right)[/tex] be 0.35?

Any help would be greatly appreciated.
Cheers
 
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  • #2
Hello,
The possible X values are x=0 and x=1, so if you compute

[tex]P_x \left( 0 \right) = p(0,0)+p(0,1)+p(0,2)=X1[/tex] Find x1
[tex]P_x \left( 1 \right) = p(1,0)+p(1,1)+p(1,2)=X2[/tex] Find x2
*You basically do this for how many possible X values you have.

Then the marginal pmf is then
[tex]P_x \left( x \right) = \left\{ x1forx= 0; x2forx=1;0,otherwise} [/tex]

Then compute the marginal pmf of Y obtained from the column totals. Hope that makes sense.
 
Last edited:
  • #3
thanks for your response :)

So I should define the marginal pmf's as?

[tex]
P_X \left( x \right) = \left\{ {\begin{array}{*{20}c}
{0.45\;...\;x = 0} \\
{0.55\;...\;x = 1} \\
\end{array}} \right.
[/tex]

[tex]
P_Y \left( y \right) = \left\{ {\begin{array}{*{20}c}
{0.35\;...\;y = 0} \\
{0.3\;...\;y = 1} \\
{0.35\;...\;y = 2} \\
\end{array}} \right.
[/tex]
 
  • #4
Yes, that's correct. From what I've been taught, you also have to put {0 otherwise} but depending on how the notation that you've been taught in class/book, then it's fine.

Also, for the marginal pmf of Y you can also put for {.35 y = 0,2 . Again, a notational way to write it.
 
Last edited:
  • #5
Yep sure, that makes sense, thanks for your help!
 

Related to Finding Marginal Probability Mass Functions for Random Variables X and Y

What is a joint distribution?

A joint distribution is a statistical concept used to describe the relationship between two or more variables. It shows the probability of each combination of values occurring for the variables.

How is a joint distribution different from a marginal distribution?

A joint distribution considers the probabilities for all combinations of values for multiple variables, while a marginal distribution only considers the probabilities for one variable at a time.

How is a joint distribution calculated?

The joint distribution is calculated by multiplying the probabilities of each variable occurring together. For example, if the probability of event A is 0.5 and the probability of event B is 0.3, the joint probability of A and B occurring is 0.5 * 0.3 = 0.15.

What is the purpose of a joint distribution?

The purpose of a joint distribution is to understand the relationship between two or more variables and to calculate the probability of different combinations of values occurring.

How can a joint distribution be visualized?

A joint distribution can be visualized through a scatter plot or a contour plot, showing the different combinations of values and their associated probabilities.

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