Is P(X<Y) Equal to 1/3 for f(x,y) = e^{-x-2y}?

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Homework Help Overview

The discussion revolves around determining the probability P(X

Discussion Character

  • Exploratory, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • Participants are examining the calculation of P(X

Discussion Status

The discussion is ongoing, with participants providing insights into the normalization of the probability density function and questioning the setup of the problem. Some participants suggest alternative approaches to defining the cumulative distribution function.

Contextual Notes

There are conflicting views on the domain of the function and its implications for the normalization of the probability density function. Some participants assert that the function does not integrate to 1, while others propose adjustments to the bounds to achieve normalization.

tronter
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If [tex]f(x,y) = e^{-x-2y}[/tex] find [tex]P(X<Y)[/tex].

So is this equaled to [tex]1 - P(X>Y) = 1 - \int\limits_{0}^{\infty} \int\limits_{0}^{x} e^{-x-2y} \ dy \ dx = \frac{1}{3}?[/tex]
 
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What is the domain? [itex]\int_0^\infty\int_0^\infty f(x,y)dydx[/itex]=1/2, not 1.
 
the domain is [tex]0 < x < \infty[/tex], [tex]0 < y < \infty[/tex]. It is equaled to [tex]1[/tex], so its a valid pdf.
 
On that domain the probability doesn't add up to 1.
 
whoops, I meant [tex]-\infty < x < \infty[/tex], [tex]-\infty < y< \infty[/tex].
 
It looks like the lower bound should be -Log[2]/3.

For a "suitable" lower bound that equates the double integral to 1, your formula is correct.

Alternatively, since integration over [0, +infinity) gives 1/2, you might define the cumulative distribution F*(x,y) = F(x,y) + 1/2, where F(x,y) is the double integral of f(s,t) over s from 0 to x and t from 0 to y. In this case f is a valid pdf.
 
Last edited:
forgetting about the bounds for the moment, is the general set up correct?
 
See my previous post (edited).
 

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