PDF of function of 3 continuous, uniform random variables?

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Discussion Overview

The discussion revolves around proving that the random variable (XY)^Z, where X, Y, and Z are independent continuous uniform random variables on (0,1), is also uniformly distributed on (0,1). Participants explore the probability density function (pdf) of the product XY and how it relates to the distribution of (XY)^Z.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Homework-related

Main Points Raised

  • One participant expresses uncertainty about how to approach the problem, particularly regarding the integration and combination of pdfs.
  • Another participant suggests starting with the expectation of the probability P(WZ ≤ x) and proposes a method to calculate it using the independence of Z and W.
  • A different participant outlines a method to find the cumulative distribution function (CDF) of W by conditioning on Y and calculating probabilities based on the values of Y.
  • The pdf for W is derived as -ln(w) through differentiation of the CDF.
  • Participants discuss the implications of their calculations and how they relate to the original problem without reaching a definitive conclusion on the uniformity of (XY)^Z.

Areas of Agreement / Disagreement

Participants present various methods and calculations, but there is no consensus on the overall proof that (XY)^Z is uniformly distributed on (0,1). The discussion reflects differing approaches and interpretations of the problem.

Contextual Notes

Some assumptions regarding the independence of the random variables and the conditions under which the calculations are valid are not explicitly stated. The derivation of the pdf and the integration steps may depend on specific interpretations of the random variables involved.

Phillips101
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Hi. The question is:

Given X, Y and Z are all continuous, independent random variables uniformly distributed on (0,1), prove that (XY)^Z is also uniformly distributed on (0,1).

I worked out the pdf of XY=W. I think it's -ln(w). I have no idea at all how to show that W^Z is U(0,1).

What do I integrate, how do I know how to combine the pdfs, how do I know what the limits are, what substitutions should I make if I need to make one? Etc, really. I just don't know how to tackle this sort of problem at all. The pdf I have for W came from a picture, not any real understanding of what I was doing.

Thanks for any help :)
 
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Fix x in (0,1). We could start with

P(WZ ≤ x) = E[P(WZ ≤ x | W)].

Since Z and W are independent, we can calculate P(WZ ≤ x | W) by treating W as a constant. In this case, if W > x, then the probability is 0. Otherwise, WZ ≤ x iff Z ≥ ln(x)/ln(W), which has probability 1 - ln(x)/ln(W). Hence,

[tex] \begin{align*}<br /> E[P(W^Z \le x \mid W)] &= E\left[{\left({1 - \frac{\ln(x)}{\ln(W)}}\right)1_{\{W\le x\}}}\right]\\<br /> &= \int_0^x \left({1 - \frac{\ln(x)}{\ln(w)}}\right)(-\ln(w))\,dw.<br /> \end{align*}[/tex]

Now do the integral and check that the result is x.
 
Phillips101 said:
The pdf I have for W came from a picture, not any real understanding of what I was doing.
We can do this the same way. If [tex]w\in(0,1)[/tex], then

[tex]\begin{align*}<br /> P(W\le w) &= P(XY \le w)\\<br /> &= E[P(XY\le w \mid Y)]\\<br /> &= E\left[{P\left({X\le\frac wY\mid Y}\right)}\right].<br /> \end{align*}[/tex]

If [tex]Y\le w[/tex], then the probability is 1; otherwise, it is w/Y. Thus,

[tex]\begin{align*}<br /> P(W\le w) &= E\left[{1_{\{Y\le w\}} + \frac wY1_{\{Y > w\}}}\right]\\<br /> &= P(Y \le w) + \int_w^1 \frac wy\,dy\\<br /> &= w - w\ln(w).<br /> \end{align*}[/tex]

To get the density, we differentiate, which gives [tex]-\ln(w)[/tex].
 
Thanks a lot, that's really very useful.

James
 

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