Finding PDF of uniform distribution

In summary, the conversation discusses how a new random variable Y is created from a uniform random variable X using the function g(x) = -a.ln(x). It is shown that Y follows an exponential distribution and the mean of Y is discussed. The solution involves using the fact that the probability for a given interval dx and corresponding interval dy must be the same.
  • #1
electroissues
7
0

Homework Statement



Let X be a uniform random variable in the interval [0,1] i.e., X = U [(0,1)]. Then a new random variable Y is given by Y= g(X), where g(x)= -a. ln(x). Show that Y is exponentially distributed. What is the mean of Y?



Homework Equations



fX(x) = 1/ lambda . exp (-x/ lambda)

0 otherwise


The Attempt at a Solution



Y = g(X) can be computed via FY(y)= Summation 1/ differentiation of g(x) multiplied with Fx(x)
I can't show how this is exponential.

Please help!
 
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  • #2
so as its constant doesn't [itex]f_X(x)=1[/itex] in [0,1], zero otherwise

then use the fact that the probabilty for a given interval dx and corresponding interval dy must be the same
[tex] |f_X(x)dx| = |f_Y(y)dy| [/tex]
 
  • #3
is that what you tried to do? can you show your working?
 

1. What is a uniform distribution?

A uniform distribution is a probability distribution where all possible outcomes have an equal chance of occurring. This means that the data is evenly spread out over a given range and there are no significant peaks or valleys.

2. How do you find the PDF of a uniform distribution?

The PDF (probability density function) of a uniform distribution can be calculated by dividing 1 by the total range of the distribution. This gives each data point an equal probability of occurring within the range.

3. What is the formula for the PDF of a uniform distribution?

The formula for the PDF of a uniform distribution is f(x) = 1 / (b-a), where a and b are the minimum and maximum values of the range, respectively.

4. Can the PDF of a uniform distribution be negative?

No, the PDF of a uniform distribution cannot be negative. Since the probability of each data point is equal, the PDF will always be positive or zero.

5. How is the PDF of a uniform distribution related to the CDF?

The PDF and CDF (cumulative distribution function) are related by the derivative and integral, respectively. The PDF is the derivative of the CDF, and the CDF is the integral of the PDF. This means that the CDF can be calculated by integrating the PDF over a given range.

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