Marginal Probability function?Anyone

In summary: You might want to check this. It won't change the method, but it will change the result. I am sorry I didn't see it earlier.
  • #1
Mathemag1c1an
7
0
Marginal Probability function?Anyone

I have this question which I cannot seem to solve:
The joint probability mass function p(x, y) of two discrete random variables X and Y is given by.
p(x,y) = ([5^x][7^y][e^-5])/x!(y-x)!
x and y are non-negative integers and x <= y
(i) Find the marginal probability mass functions of X and Y.
 
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  • #2


Sum p(x,y) over x from 0 to y to get the marginal for Y. Sum p(x,y) over y from x to oo to get the marginal for X.
 
  • #3


but how do we integrate the x!(y-x)!
 
  • #4


Mathemag1c1an said:
but how do we integrate the x!(y-x)!


Remember we are dealing with integers, so we have to carry out summations.

x summation to get marginal for Y: x terms are 5^x/[!(y-x)!] the sum from 0 to y can be gotten from the binomial expansion of (1+5)^y = sum 5^x[yCx], where yCx is the combinatorial symbol =y!/[x!(y-x)!].
Put this together and you have P(Y=y) = (e^-5)(42^y)/y!
This is wrong, since the total probability is not 1. I suggest you examine your original description.

y summation to get marginal for X: sum from x to oo of 7^y/(y-x)!
which is simply (7^x)(e^7).
This leads to P(X=x) = (e^2)(35^x)/x!. This also is wrong.

These could be corrected if e^-5 is replaced by e^-42.
 
  • #5


I knew something was wrong with the question:
 
Last edited:
  • #6


Thanks alot
 
  • #7


p(x,y) = ([5^x][7^y][e^-5])/x!(y-x)!

If you write it in the form p(x,y) = ([a^x][b^y][e^-c])/x!(y-x)!, you need b(a+1)=c for it to be valid.
 

Related to Marginal Probability function?Anyone

What is a marginal probability function?

A marginal probability function is a mathematical representation of the probability distribution of a single random variable in a multi-dimensional distribution. It shows the probability of a single variable occurring, regardless of the values of the other variables in the distribution.

How is a marginal probability function calculated?

To calculate a marginal probability function, the probability of each possible value of the variable of interest is summed up across all other variables in the distribution. This can be done using a joint probability function or a contingency table, depending on the type of distribution.

What is the difference between a marginal probability function and a conditional probability function?

A marginal probability function shows the probability of a single variable occurring without taking into account the values of other variables in the distribution. A conditional probability function, on the other hand, shows the probability of a variable occurring given that another variable has a specific value.

What is the importance of marginal probability functions in statistics?

Marginal probability functions are important in statistics because they allow us to analyze the distribution of a single variable in a multi-dimensional dataset. They also help us understand the relationship between different variables in a distribution and can be used for hypothesis testing and making predictions.

Can marginal probability functions be used for continuous variables?

Yes, marginal probability functions can be used for both discrete and continuous variables. For continuous variables, the function is represented as a probability density function (PDF) rather than a probability mass function (PMF) as in the case of discrete variables.

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