Joint Probability functions (finding marginals)

In summary, the problem involves finding the marginal distributions (pdf and cdf) of X and Y and solving for Pr(X+Y>0.5). The marginal distribution of X is found to be ln(y), with ln(0) not existing and ln(1) equal to 0. The second part of the problem involves solving for Pr(X+Y>0.5) where the integration limits need to be reconsidered due to the non-zero values of the function only being within the triangle defined by x=0, y=1, and y=x.
  • #1
Ryuuzakie
3
0

Homework Statement


[tex] f\left(x,y\right)=\left\{\begin{array}{cc}\frac{1}{y} ,&\mbox{ if }
0 \leq x \leq y, 0 \leq y \leq 1 \\ 0,&mbox{ otherwise } \end{array}\right. [/tex]

Find the marginal distributions (pdf and cdf) of X and Y

Find Pr(X+Y>0.5)

Homework Equations


N/A

The Attempt at a Solution



Finding the marginal distribution of X, I get,

[tex]f_X(x) = \int_{0}^{1} \frac{1}{y} dx [/tex]
[tex]f_X(x) = ln \left(y\right)\right|_0^1 [/tex]

But ln (0) does not exist, and ln (1) = 0... so I'm thinking I'm doing this wrong.

Also, with the second part of the problem, I get:
[tex]Pr(X+Y \geq 0.5)=1- \left[0.5 ln \left(y\right) - y\right]_0^1[/tex]

And as before, I'm stuck with an ln(0).. any assistance would be appreciated :)!
 
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  • #2
note that [itex] f_{X,Y}(x,y) [\itex] is only non-zero for the triangle given by x = 0, y= 1 & y=x

so you may need to rethink your integration limits...
 

What is a joint probability function?

A joint probability function is a mathematical representation of the probability that two or more events will occur simultaneously. It takes into account the probabilities of each individual event and their relationship to each other.

How do you find the marginal probability from a joint probability function?

To find the marginal probability from a joint probability function, you simply sum up the probabilities of all outcomes that include the event of interest. For example, if you are interested in the marginal probability of event A, you would sum up all probabilities where event A occurs, regardless of the outcome of other events.

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

A joint probability function calculates the probability of multiple events occurring together, whereas a conditional probability function calculates the probability of one event occurring given that another event has already occurred.

Can you have a joint probability function for more than two events?

Yes, a joint probability function can be used for any number of events. However, as the number of events increases, the calculations can become more complex and difficult to interpret.

How are joint probability functions used in real-world applications?

Joint probability functions are commonly used in fields such as statistics, economics, and engineering to model and analyze the relationships between multiple events. They can be used to make predictions, assess risk, and inform decision-making processes.

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