Finding the PDF of X Given an Expression

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Discussion Overview

The discussion revolves around finding the probability density function (pdf) of a random variable \(X\) defined by a specific expression involving exponential random variables. Participants explore the conditions under which the cumulative distribution function (cdf) can be derived and the implications of independence among the random variables involved.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant presents an expression for \(X\) and attempts to derive its cdf, seeking validation of their approach.
  • Another participant notes that the validity of the approach hinges on the independence of the random variables involved, indicating that if they are not independent, the factorization of the joint pdf is incorrect.
  • There is a correction regarding the limits of integration, with one participant pointing out a typo in the lower limit for integration.
  • A later reply confirms that the random variables are independent and identically distributed, suggesting that the initial approach is correct with the noted adjustments.
  • One participant emphasizes the importance of using uppercase letters for random variables to avoid confusion with quantiles.

Areas of Agreement / Disagreement

Participants generally agree on the need for independence of the random variables for the approach to be valid. However, there is an ongoing discussion about the implications of this independence and the correct notation to use, indicating that some aspects remain contested.

Contextual Notes

Participants have not fully resolved the implications of the independence assumption and its impact on the derivation of the pdf. The discussion also highlights the importance of notation in mathematical expressions.

EngWiPy
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Hi.

I have the following expression

X=\frac{G\frac{x_1}{x_2}\gamma_Q}{\frac{1}{G}x_3 \gamma_P+1}

where ##x_i## is an exponential random variable with mean 1. All other parameters are nonzero positive constants. Basically I want to find the probability density function (pdf) of ##X##. So I started with cumulative distribution function (cdf) as

F_X(x)=\text{Pr}\left[\frac{G\frac{x_1}{x_2}\gamma_Q}{\frac{1}{G}x_3 \gamma_P+1}\leq x\right]

which I evaluated it as

F_X(x)=\int_{x_3=0}^{\infty}\int_{x_3=0}^{\infty}\text{Pr}\left[x_1\leq\frac{x_2\,x}{G \gamma_Q}(\frac{1}{G}x_3 \gamma_P+1)\right]f_{X_2}(x_2)f_{X_3}(x_3)\,dx_2\,dx_3

where ##f_{X_i}(x_i)## is the pdf of the random variable ##x_i##. Is what I did correct?

Thanks
 
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It is only correct if the three random variables are independent. If they are, it is valid to factorise the joint pdf ##f_{X_1,X_2,X_3}(x_1,x_2,x_3)## into ##f_{X_1}(x_1)f_{X_2}(x_2)f_{X_3}(x_3)## as you have done. If they are not independent, that factorisation is incorrect.

Also, one of your two lower limits for integration needs to be ##x_2=0##.
 
andrewkirk said:
It is only correct if the three random variables are independent. If they are, it is valid to factorise the joint pdf ##f_{X_1,X_2,X_3}(x_1,x_2,x_3)## into ##f_{X_1}(x_1)f_{X_2}(x_2)f_{X_3}(x_3)## as you have done. If they are not independent, that factorisation is incorrect.

Also, one of your two lower limits for integration needs to be ##x_2=0##.

Yes, they are independent and identically distributed random variables. Right, the first integral is over all values of ##x_2##, it's a typo. Thanks
 
S_David said:
Yes, they are independent and identically distributed random variables. Right, the first integral is over all values of ##x_2##, it's a typo. Thanks
Given that additional info, and the discussed correction to the lower integration limit, what you've written is correct, but it is better practice to use upper case for random variables, to distinguish them from quantiles of those variables, ie:

$$F_X(x)=\int_{x_3=0}^{\infty}\int_{x_2=0}^{\infty}\text{Pr}\left[X_1\leq\frac{x_2\,x}{G \gamma_Q}(\frac{x_3 \gamma_P}{G}+1)\right]f_{X_2}(x_2)f_{X_3}(x_3)\,dx_2\,dx_3$$
 
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