What is the Marginal PMF for X1 and X2 with a Joint PMF of p^2q^x2?

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In summary: I meant. sorry for the typo. so we havef_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{i}=p^{2}\left(\dfrac{1-q^{x_{2}+1}}{1-q}\right)=p^{2}\left(\dfrac{1-q^{x_{2}+1}}{p}\right)=pq^{x_{2}+1}.In summary, the joint probability mass function for X1 and X2 is given by f(x_{1},x_{2})=p^{2}q^{x_{2}},x_{1}=0,1,
  • #1
phiiota
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Homework Statement


Suppose that X1 and X2 have the joint pmf
[tex]f(x_{1},x_{2})=p^{2}q^{x_{2}},x_{1}=0,1,2,...,x_{2},x_{2}=0,1,2,...[/tex]
with
[tex]0<p<1,q=1-p[/tex]


Homework Equations





The Attempt at a Solution


I'm confused because the expression doesn't have x_1 in it. So usually, if I want to find f1x(1), say, I would add up all the x_2 probabilities where x_1=1 to get my marginal (summing over all values of x_2). But without having an x_1 in my pmf, I don't know how to do this. I'm tempted to say the marginal is just the original pmf, but how would I differentiate between p(x_1=1,x_2=2) from p(x_1=0,x_2=2)?
 
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  • #2
There must be a typo somewhere. If you sum [itex]f(x_1, x_2)[/itex] over [itex]x_1[/itex] and [itex]x_2[/itex], the result will be infinity, not 1. Therefore this is not a valid probability mass function
 
  • #3
jbunniii said:
There must be a typo somewhere. If you sum [itex]f(x_1, x_2)[/itex] over [itex]x_1[/itex] and [itex]x_2[/itex], the result will be infinity, not 1. Therefore this is not a valid probability mass function

No, that is not the case: x1 is summed from 0 to x2, and x2 is summed from 0 to ∞.

RGV
 
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  • #4
Ray Vickson said:
No, that is not the case: x1 is summed from 1 to x2, and x2 is summed from 1 to ∞.

RGV

Oops, you're right. I misread the range for [itex]x_1[/itex].
 
  • #5
okay, so my first thought is that the joint then should be
[tex]\sum_{x_{2}=0}^{\infty}\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}[/tex]

when I let p=.5, this term does indeed sum to 1, so that's looking alright.
oh, I think I maybe figured this out. So I should have (summing over values in the domain
[tex]f_{1}(x_{1})=\sum_{x_{2}=0}^{\infty}p^{2}q^{x_{2}}[/tex]
[tex]f_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}[/tex]

No, I'm still confused. The f1 sums out to .5 (when I let p=.5). So then I should sum .5 from... 0 to x2 to get 1? But then I'll have x2 in my term, and not 1.
When I integrate f2 from 0 to infinity, I do get one, which I think is good, but I'm still confused by not having an x1 term anywhere.
 
  • #6
phiiota said:
okay, so my first thought is that the joint then should be
[tex]\sum_{x_{2}=0}^{\infty}\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}[/tex]
Yes, that's the sum of the joint pmf.

oh, I think I maybe figured this out. So I should have (summing over values in the domain
[tex]f_{1}(x_{1})=\sum_{x_{2}=0}^{\infty}p^{2}q^{x_{2}}[/tex]
No, you aren't summing over the right range of indices. Try drawing a picture of the region where the joint pmf is nonzero. Then it will be easier to see what the upper and lower limits for [itex]x_2[/itex] should be in the sum.
[tex]f_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}[/tex]
Yes, this is correct. The summand does not depend on [itex]i[/itex], so you are simply summing [itex]x_2 + 1[/itex] copies of the same thing. What is the result?
 
  • #7
Okay, so looking at a picture, I know that P(x1,x2)=0 whenever x1>x2 (and x1,x2>0). So I have kind of a triangular set up. So looking at these values, then, I'm thinking that my marginal for x should be
[tex]f_{1}(x_{1})=\sum_{i=x_{1}}^{\infty}p^{2}q^{x_{1}}[/tex]
 
  • #8
phiiota said:
Okay, so looking at a picture, I know that P(x1,x2)=0 whenever x1>x2 (and x1,x2>0). So I have kind of a triangular set up. So looking at these values, then, I'm thinking that my marginal for x should be
[tex]f_{1}(x_{1})=\sum_{i=x_{1}}^{\infty}p^{2}q^{x_{1}}[/tex]
Almost, except the exponent for [itex]q[/itex] is wrong: it should be [itex]q^i[/itex].

You should be able to find a closed form expression for each pmf. For [itex]f_{1}(x_{1})[/itex], observe that you can pull [itex]p^2[/itex] outside the sum, and you're left with [itex]p^{2}\sum_{i=x_{1}}^{\infty}q^i[/itex]. How does this sum relate to a geometric series?
 
  • #9
okay, so finally, we have that
[tex]f_{1}(x_{1})=\sum_{i=x_{1}}^{\infty}p^{2}q^{i}=p^{2}\sum_{i=x_{1}}^{\infty}q^{i}[/tex]
so [tex]\sum_{i=0}^{\infty}q^{i}=\dfrac{1}{1-(1-p)}=\dfrac{1}{p}. \sum_{i=0}^{x_{1}}q^{i}=\dfrac{1-q^{x_{1}-1}}{1-q}=\dfrac{1-q^{x_{1}-1}}{p}[/tex], so taking the difference, we have
[tex]f_{1}(x)=p^{2}\left(\dfrac{q^{x_{1}-1}}{p}\right)=pq^{x_{1}-1}[/tex].

for f_2(x_2), we just have
[tex]f_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}=(x_{2}+1)p^{2}q^{x_{2}}[/tex]

is this right? thanks for your help.
 
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  • #10
phiiota said:
okay, so finally, we have that
[tex]f_{1}(x_{1})=\sum_{i=x_{1}}^{\infty}p^{2}q^{i}=p^{2}\sum_{i=x_{1}}^{\infty}q^{i}[/tex]
so [tex]\sum_{i=0}^{\infty}q^{i}=\dfrac{1}{1-(1-p)}=\dfrac{1}{p}.[/tex]
Yes.
[tex]\sum_{i=0}^{x_{1}}q^{i}=\dfrac{1-q^{x_{1}-1}}{1-q}=\dfrac{1-q^{x_{1}-1}}{p}[/tex],
The upper limit on your sum should be [itex]x_1 - 1[/itex], not [itex]x_1[/itex]. Otherwise you will lose the [itex]x_1[/itex] term when you take the difference.
for f_2(x_2), we just have
[tex]f_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}=(x_{2}+1)p^{2}q^{x_{2}}[/tex]
Yes, but you need to specify the range of values of [itex]x_2[/itex] for which this formula is valid. For example, if [itex]x_2[/itex] is negative, this formula isn't right.
 
  • #11
phiiota said:
okay, so finally, we have that
[tex]f_{1}(x_{1})=\sum_{i=x_{1}}^{\infty}p^{2}q^{i}=p^{2}\sum_{i=x_{1}}^{\infty}q^{i}[/tex]
so [tex]\sum_{i=0}^{\infty}q^{i}=\dfrac{1}{1-(1-p)}=\dfrac{1}{p}. \sum_{i=0}^{x_{1}}q^{i}=\dfrac{1-q^{x_{1}-1}}{1-q}=\dfrac{1-q^{x_{1}-1}}{p}[/tex], so taking the difference, we have
[tex]f_{1}(x)=p^{2}\left(\dfrac{q^{x_{1}-1}}{p}\right)=pq^{x_{1}-1}[/tex].

for f_2(x_2), we just have
[tex]f_{2}(x_{2})=\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}=(x_{2}+1)p^{2}q^{x_{2}}[/tex]

is this right? thanks for your help.

What you have written makes no sense: your summation gives the wrong answer
[tex]\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}= (x_2+1) p^2 q^{x_2};[/tex] do you see why?
Maybe you intended to say
[tex]f_2(x_2) = \sum_{i=0}^{x_2}p^2 q^i,[/tex]
in which case that is what you should have written.

BTW: it will save you a lot of time and trouble if you use only the needed brackets in LaTeX. When the subscript or superscript is a single symbol, you do not need curly brackets, so it is OK to write f_2(x_2) and q^i, for example, but you DO need brackets when you have things like \sum_{i=0}^{x_2}, because the sub- or super-scripts have more than one symbol. However, you do NOT need to write ^{x_{2}}.

RGV
 
  • #12
Ray Vickson said:
What you have written makes no sense: your summation gives the wrong answer
[tex]\sum_{i=0}^{x_{2}}p^{2}q^{x_{2}}= (x_2+1) p^2 q^{x_2};[/tex] do you see why?
Assuming the joint pmf is correctly stated in the original post, this is in fact a valid expression for the pmf of [itex]x_2[/itex]. What makes you say that [itex](x_2+1) p^2 q^{x_2}[/itex] (for [itex]x_2 \geq 0[/itex], otherwise 0) is the wrong answer? It's certainly a valid pmf: it takes on only nonnegative values and it sums to 1.
 
  • #13
jbunniii said:
Assuming the joint pmf is correctly stated in the original post, this is in fact a valid expression for the pmf of [itex]x_2[/itex]. What makes you say that [itex](x_2+1) p^2 q^{x_2}[/itex] (for [itex]x_2 \geq 0[/itex], otherwise 0) is the wrong answer? It's certainly a valid pmf: it takes on only nonnegative values and it sums to 1.

OK, right! I should not post anything before my first cup of coffee!

RGV
 
  • #14
Thanks. As far as the latex goes, I usually type it in an editor, and then paste the code in here.
 

What is a marginal pmf?

A marginal pmf, or probability mass function, is a statistical tool used to describe the distribution of a single variable in a larger set of variables.

How do you calculate a marginal pmf?

To calculate a marginal pmf, you first need to identify the variable of interest and then sum up the probabilities of all possible outcomes for that variable while holding the other variables constant.

What is the purpose of finding a marginal pmf?

Finding a marginal pmf allows us to understand the distribution of a single variable in a larger set of variables, which can help us make predictions and draw conclusions about the overall system.

Can a marginal pmf be used for continuous variables?

No, marginal pmfs are only applicable for discrete variables with a finite or countably infinite number of possible outcomes. For continuous variables, we use probability density functions instead.

Can a marginal pmf be used to determine causality?

No, a marginal pmf only describes the relationship between variables and does not establish causality. Other statistical tools, such as experiments and regression analysis, are needed to determine causality.

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