Marginal distribution function

In summary, a marginal distribution function is a statistical concept that describes the probability distribution of a single variable in a multivariate dataset. It differs from a joint distribution function, which focuses on the probability of multiple variables, and can be used to calculate conditional distribution functions. Marginal distribution functions can be graphically represented and have various applications in data analysis, such as identifying patterns and making predictions. They are also commonly used in statistical modeling and hypothesis testing.
  • #1
_joey
44
0
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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  • #2
I solved the problem.
 
  • #3
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
 
  • #4
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'
 
  • #5
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:
  • #6
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.
 
  • #7
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]
 

1. What is a marginal distribution function?

A marginal distribution function is a statistical concept that describes the probability distribution of a single variable in a multivariate dataset. It represents the probability of that variable taking on a specific value or falling within a certain range of values.

2. How is a marginal distribution function different from a joint distribution function?

A joint distribution function describes the probability of two or more variables taking on specific values simultaneously, while a marginal distribution function focuses on the probability of only one variable. It is obtained by summing or integrating the joint distribution function over all possible values of the other variables.

3. What is the relationship between marginal distribution functions and conditional distribution functions?

The marginal distribution function of a variable can be used to calculate the conditional distribution function of that variable, which describes the probability of that variable taking on a specific value or falling within a certain range of values given that another variable has a fixed value. This relationship is known as the chain rule of probability.

4. How can a marginal distribution function be represented graphically?

A marginal distribution function can be represented graphically using a histogram, bar chart, or line graph. These visualizations show the probability of the variable taking on different values or falling within different intervals, providing a visual understanding of the distribution.

5. What are the applications of marginal distribution functions in data analysis?

Marginal distribution functions are commonly used in data analysis to understand the distribution of variables and their relationships with other variables. They are useful for identifying patterns, making predictions, and drawing conclusions about a dataset. They are also used in statistical modeling and hypothesis testing.

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