Calculating Conditional Probability with Joint Probability Density Function

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In summary: So then the final answer is \dfrac{\int_0^1 \int_0^y 2e^{-(x+y)} dxdy}{\int_0^1 2e^{-x} dx} = \dfrac{1-e^{-2}}{e^{-1}} = e-1 In summary, the conversation discusses finding the probability, P(Y<1 | X<1), using the formula P(A|B) = \frac{P(A\, \& \, B)}{P(B)}. Using appropriately-defined A and B, the final answer is e-1.
  • #1
mrkb80
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Homework Statement



let [itex]f_{X,Y}(x,y)=2e^{-(x+y)} [/itex] for [itex] 0 \le x \le y[/itex] and [itex] y \ge 0 \\

[/itex]find [itex]P(Y<1 | X < 1)
[/itex]

Homework Equations


[itex]
f(X=x | y=y) = \dfrac{f_{X,Y}(x,y)}{f_y(y)}
[/itex]

The Attempt at a Solution


[itex]P(Y<1 | X<1) = \int_0^1 \dfrac{f_{X,Y}(x,y)}{\int_0^1 f_X(x) dx} dy [/itex]

before I integrate, I want to make sure I understand the concept, which I don't think I do.
 
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  • #2
mrkb80 said:

Homework Statement



let [itex]f_{X,Y}(x,y)=2e^{-(x+y)} [/itex] for [itex] 0 \le x \le y[/itex] and [itex] y \ge 0 \\

[/itex]find [itex]P(Y<1 | X < 1)
[/itex]

Homework Equations


[itex]
f(X=x | y=y) = \dfrac{f_{X,Y}(x,y)}{f_y(y)}
[/itex]

The Attempt at a Solution


[itex]P(Y<1 | X<1) = \int_0^1 \dfrac{f_{X,Y}(x,y)}{\int_0^1 f_X(x) dx} dy [/itex]

before I integrate, I want to make sure I understand the concept, which I don't think I do.

Use the formula
[tex] P(A|B) = \frac{P(A\, \& \, B)}{P(B)}. [/tex]
with appropriately-defined A and B.

RGV
 
  • #3
I'm thinking that would be something like this:

P(A) = P(Y<1)
P(B) = P(X<1)=[itex]\int_0^1 f_X(x) dx [/itex]
so then [itex]P(A \cap B) =P(Y<1 \cap X<1) = \int_0^1 \int_0^y f_{X,Y}(x,y) dxdy [/itex]

and then [itex] \dfrac{\int_0^1 \int_0^y f_{X,Y}(x,y) dxdy}{\int_0^1 f_X(x) dx}[/itex]

or am I still not understanding?
 
  • #4
mrkb80 said:
I'm thinking that would be something like this:

P(A) = P(Y<1)
P(B) = P(X<1)=[itex]\int_0^1 f_X(x) dx [/itex]
so then [itex]P(A \cap B) =P(Y<1 \cap X<1) = \int_0^1 \int_0^y f_{X,Y}(x,y) dxdy [/itex]

and then [itex] \dfrac{\int_0^1 \int_0^y f_{X,Y}(x,y) dxdy}{\int_0^1 f_X(x) dx}[/itex]

or am I still not understanding?

This is OK. Now all you need do is find fX, and do the integrations in the numerator and the denominator.

RGV
 
  • #5
Great. Thanks for the help again.
 
  • #6
I thought I understood it, but something is not correct. just working on the denominator, I get [itex]f_X(x)=-2(e^{-(x+y)}-e^{-x}) [/itex] and then if I try to integrate that I get [itex]\int_0^1 f_X(x) dx = 2e^{-y-1}+2e^{-y}+e^{-1}+1[/itex] What am I missing here?
 
  • #7
mrkb80 said:
I thought I understood it, but something is not correct. just working on the denominator, I get [itex]f_X(x)=-2(e^{-(x+y)}-e^{-x}) [/itex] and then if I try to integrate that I get [itex]\int_0^1 f_X(x) dx = 2e^{-y-1}+2e^{-y}+e^{-1}+1[/itex] What am I missing here?

You are missing the fact that f_X(x) cannot have y in it. You need to start again.

RGV
 
  • #8
You're right. I see my mistake: [itex]f_X(x)=2e^{-x} [/itex] makes much more sense.
 

What does P(Y<1 | X<1) represent?

P(Y<1 | X<1) represents the probability of the random variable Y being less than 1, given that the random variable X is also less than 1.

How is P(Y<1 | X<1) calculated?

P(Y<1 | X<1) is calculated by dividing the number of outcomes where both Y and X are less than 1 by the total number of outcomes where X is less than 1. This is known as conditional probability.

What is the significance of P(Y<1 | X<1) in statistical analysis?

P(Y<1 | X<1) is important in statistical analysis because it allows us to understand the relationship between two variables and how one variable affects the other. It also helps us to make predictions and draw conclusions about the data.

Can P(Y<1 | X<1) be greater than 1?

No, P(Y<1 | X<1) cannot be greater than 1. Since it is a probability, its value must be between 0 and 1. If P(Y<1 | X<1) is calculated to be greater than 1, then there is an error in the calculations.

How does changing the value of X affect P(Y<1 | X<1)?

Changing the value of X can affect P(Y<1 | X<1) in different ways. If X is increased, the probability of Y being less than 1 may also increase or decrease, depending on the relationship between X and Y. If X is decreased, the probability of Y being less than 1 may also increase or decrease, again depending on the relationship between X and Y.

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