Calculating Marginal Probability Mass Functions for Discrete Random Variables

In summary, the conversation discusses finding the marginal probability mass functions of two discrete random variables, X and Y, with a given joint probability mass function. The method for finding the marginal probabilities involves integrating out the dependence on the second variable and summing the probabilities for each variable. The conversation also mentions an error in the joint pmf calculation and questions how to integrate the factorials.
  • #1
Mathemag1c1an
7
0
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
You would have to "integrate out" the dependence on the second variable. Explicitly

[tex]
p(x)=\sum_{y\geq x}p(x,y)
[/tex]
and
[tex]
p(y)=\sum_{x\leq y}p(x,y)
[/tex]
By the way, you joint pmf doesn't sum to one, but to e37.
 
  • #3
but how do i integrate the factorials?
 

FAQ: Calculating Marginal Probability Mass Functions for Discrete Random Variables

What is the Marginal Probability Function?

The Marginal Probability Function is a concept in statistics that describes the probability of a single event occurring based on the probabilities of multiple related events.

How is the Marginal Probability Function calculated?

The Marginal Probability Function is calculated by summing or integrating the joint probability function over all possible values of the other variables, leaving only the desired variable as the final outcome.

What is the difference between Marginal Probability and Conditional Probability?

Marginal Probability is the probability of a single event occurring, while Conditional Probability is the probability of an event occurring given that another event has already occurred. Marginal Probability is calculated by summing or integrating over all possible values of the other variables, while Conditional Probability is calculated by dividing the joint probability of two events by the probability of the first event.

Why is the Marginal Probability Function important?

The Marginal Probability Function is important because it allows us to calculate the probability of a single event occurring, even when there are multiple variables involved. It also helps us to understand the relationship between different variables and how they affect the probability of a single event.

What are some real-world applications of the Marginal Probability Function?

The Marginal Probability Function is used in many fields, including finance, economics, and medicine. It can help predict the probability of stock market trends, the likelihood of economic events, and the risk of developing certain diseases.

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