PDF of function of 3 continuous, uniform random variables?

In summary: answered a question about a continuous probability distribution and integrated it to show that the probability of a value less than a given value is the probability of the value less than the integral of the probability distribution.
  • #1
Phillips101
33
0
Hi. The question is:

Given X, Y and Z are all continuous, independant 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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  • #2
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*}
E[P(W^Z \le x \mid W)] &= E\left[{\left({1 - \frac{\ln(x)}{\ln(W)}}\right)1_{\{W\le x\}}}\right]\\
&= \int_0^x \left({1 - \frac{\ln(x)}{\ln(w)}}\right)(-\ln(w))\,dw.
\end{align*}
[/tex]

Now do the integral and check that the result is x.
 
  • #3
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*}
P(W\le w) &= P(XY \le w)\\
&= E[P(XY\le w \mid Y)]\\
&= E\left[{P\left({X\le\frac wY\mid Y}\right)}\right].
\end{align*}
[/tex]

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

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

To get the density, we differentiate, which gives [tex]-\ln(w)[/tex].
 
  • #4
Thanks a lot, that's really very useful.

James
 

1. What is a PDF?

A PDF (Probability Density Function) is a mathematical function that describes the probability distribution of a continuous random variable. It represents the relative likelihood that a variable will take on a certain value or fall within a certain range of values.

2. What does "continuous" mean in this context?

In this context, "continuous" refers to the type of random variable being described. A continuous random variable can take on any value within a certain range, as opposed to a discrete random variable which can only take on certain specific values.

3. What is a "uniform" random variable?

A "uniform" random variable is a type of continuous random variable where all values within a certain range have an equal likelihood of occurring. It is often represented by a rectangular-shaped probability distribution.

4. How do you find the PDF of a function with 3 continuous, uniform random variables?

To find the PDF of a function with 3 continuous, uniform random variables, you would first find the individual PDFs for each variable. Then, you would use the joint PDF formula to determine the overall PDF for the function. This involves multiplying the individual PDFs together and taking into account any transformations or adjustments made to the variables in the function.

5. What is the significance of the PDF of a function with 3 continuous, uniform random variables?

The PDF of a function with 3 continuous, uniform random variables allows us to understand the likelihood of certain values occurring within the given range. It is important in probability and statistics for making predictions and analyzing data.

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