How Do You Find Marginal Distribution Functions?

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Discussion Overview

The discussion revolves around finding the marginal distribution functions P(X=x) and P(Y=y) from a given joint probability function. Participants explore the calculations involved, share their attempts, and raise questions about the correct approach to summation and conditional expectations.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents the joint probability function and attempts to derive P(X=x) through summation over y, but expresses difficulty in simplifying the series.
  • Another participant claims to have solved the problem but does not provide the answer, prompting questions about the correctness of the initial summation approach.
  • Multiple participants challenge the initial summation limits, suggesting that y should start from x and go to infinity, asking for clarification on this point.
  • A new question is introduced regarding the conditional expectation E[X|Y=y], with one participant proposing a formula but expressing uncertainty about its correctness.
  • Another participant provides a graphical interpretation of the relationship between x and y, explaining how fixing x affects the possible values of y.
  • Further elaboration on calculating P(Y=y) is provided, including a method involving binomial coefficients and a summation that leads to a simplified expression for P(Y=y).
  • Finally, a participant concludes with a derived expression for E[X|Y=y], stating it as \(\frac{y}{2}\) without confirming its correctness.

Areas of Agreement / Disagreement

Participants express disagreement regarding the correct limits of summation for y and the validity of the proposed solutions. There is no consensus on the correctness of the initial approach or the final answers provided.

Contextual Notes

Some assumptions regarding the joint probability function and the conditions for summation are not fully articulated, leading to potential ambiguities in the calculations. The discussion includes unresolved mathematical steps and varying interpretations of the problem.

Who May Find This Useful

Readers interested in probability theory, particularly in the context of marginal distributions and conditional expectations, may find the discussion relevant.

_joey
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Given:

[tex]P(X=x, Y=y)=\frac{a^ye^{-2a}}{x!(y-x)!}[/tex] where [tex]x=0,1,2,...y[/tex] and [tex]y=0,1,2...\infty[/tex], and [tex]a>0[/tex]

Find [tex]P(X=x)[/tex] and [tex]P(Y=y)[/tex]

An example is provided in a book on books.google.com
Page 96
http://books.google.com.au/books?id...AEwCTgK#v=onepage&q=marginal discrete&f=false

Here is my attempted solution
[tex]p_{X}(x)=\Sigma_{y=0}^{\infty}\frac{a^ye^{-2a}}{x!(y-x)!}=e^{-2a}+ae^{-2a}+\frac{a^2e^{-2a}}{x!(2-x)!}+...+\frac{a^ne^{-2a}}{x!(n-x)!}[/tex]

And then I cannot simplify this serie. Any comments and suggestions will be very much appreciated
 
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I solved the problem.
 
what's the answer you got since your summation over y is wrong... y should go as
x, x+1,x+2... infty. for given x
 
IssacNewton said:
what's the answer you got since your summation over y is wrong... y should go as
x, x+1,x+2... infty. for given x

maybe it is wrong. Elaborate why 'y should go as x, x+1, x+2...infty'
 
I've got another question: [tex]E[X|Y=y]=?[/tex]

My answer is [tex]E[X|Y=y]=\frac{y}{2}e^{2a-2y}[/tex]

I am not sure if it is correct.

Thanks!
 
Last edited:
just plot the points (x,y) on the graph.lets say y=0, then x can take value x=0
so the plotted point would be (0,0) . now if y=1 then x=0,1 so the plotted points would be
(0,1) , (1,1). if y=2 then x=0,1,2. so the plotted points would be (0,2),(1,2),(2,2) and so on.
now the marginal distribution for x is for some given x , that means we fix the value of x and then look for the values that y can take. so on this graph, if you fix value of x to be say 5,then y must take values 5 onwards. y can't take value less than 5 because then x=5 would not be possible. if you take value of x to be 7, then y must take values 7 onwards. this is easy to see
if you draw a vertical line from some fixed value of x. you can see that , y can take values
x onwards for that fixed value of x.

once you do that, the summation is very easy.
 
now

[tex] E[X|Y=y]=\sum_{x\in X} x\, \frac{P[X=x,Y=y]}{P[Y=y]}[/tex]

so first you have to calculate [tex]P(Y=y)[/tex] here for a fixed value of y, x can take values from 0 to y.

[tex]P(Y=y)\,=\, \sum_{x=0}^y \, \frac{a^y e^{-2a}}{x!\, (y-x)!}[/tex]

[tex]= a^y e^{-2a}\, \sum_{x=0}^y \, \frac{1}{x!(y-x)!}[/tex]

now here you do a small trick. you multiply and divide by y!. since y! is not dependent on x, you pull out the denominator y! and you have y! in the numerator. then what you have is
a binomial coefficient.

[tex]\sum_{x=0}^y \, \binom{y}{x}=\, \sum_{x=0}^y \, \frac{y!}{x!(y-x)!}[/tex]

the above sum is just [tex]2^y[/tex]. so

[tex]P(Y=y)\, = \, \frac{a^y e^{-2a}}{y!} \, 2^y[/tex]

then use this to do the required summation. with the above expression, final answer I get is

[tex]E[X|Y=y]=\frac{y}{2}[/tex]
 

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