Let X be uniformly distributed on (0,1)

  • Thread starter TomJerry
  • Start date
  • Tags
    Distributed
In summary, we can use moment generating functions to show that Y has an exponential distribution with parameter \lambda>0, given that X is uniformly distributed on (0,1) and Y = - 1/(lambda) ln(1 - X). Additionally, we can also use the formula for computing the density of a strictly increasing function to derive the density of Y.
  • #1
TomJerry
50
0
Question:
Let X be uniformly distributed on (0,1). Show that Y=- [tex]\lambda[/tex]-1 1n(1-X) has an exponential distribution with parameter [tex]\lambda[/tex]>0
 
Physics news on Phys.org
  • #2
Do you know what generating functions are?
 
  • #3
let

[tex]
F_Y(y) = P(Y \le y)
[/tex]

and use the definition of [itex] Y [/itex] to rewrite the inequality in terms of [itex] X [/itex].
 
  • #4
chiro said:
Do you know what generating functions are?

Nope !
 
  • #5
Here is how you can do this with a moment generating function.

Basically you can prove that a distribution is a particular one if both moment generating functions are of the same type.

So for example you've given Y = - 1/(lambda) ln(1 - X).

We are given that X is uniform on (0,1) so basically standard uniform.

The PDF of X is simply 1 with the domain (0,1).

The MGF of Y is given by M(t) = E[e^(Yt)] = Integral [0,1] 1 x e^(-{1/lambda} x ln{1-x} x t) dx = Integral [0,1] (1-x)^(-t/{lambda})

Using a substitution we get u = 1 - x, du = -dx which gives us the integral

M(t) = Integral [0,1] u^(-t/{lambda}) du.

This gives us M(t) = 1/{-t/lambda + 1} which corresponds to the MGF of the exponential distribution.
 
  • #6
You can also use the standard formula for computing the density of a strictly increasing function. If X has density f_X(x) and Y=g(X) is strictly increasing, then Y has density f_Y(y)=f_X(g^-1(y))*dg^-1(y)/dy
 

1. What does it mean for X to be uniformly distributed on (0,1)?

Being uniformly distributed on (0,1) means that the probability of X taking on any value between 0 and 1 is equal. In other words, all values within this range have an equal chance of occurring.

2. How is the probability density function (PDF) of X determined?

The PDF of X is determined by dividing the probability of X taking on a particular value by the total range of values (1-0=1) within which X is uniformly distributed. This results in a constant value of 1 for all values of X within the range of (0,1).

3. What is the expected value of X?

The expected value, or mean, of X is calculated by taking the average of all possible values of X within the range of (0,1). In this case, it would be (0+1)/2 = 0.5. This means that 0.5 is the most likely value for X to occur.

4. Can X take on values outside of the range (0,1)?

No, X cannot take on values outside of the range (0,1) because it is uniformly distributed within this range. This means that the probability of X taking on a value outside of (0,1) is 0.

5. How is the uniform distribution of X useful in scientific research?

The uniform distribution of X is useful in scientific research because it allows for a simple and consistent way to model and analyze random phenomena. It is often used as a baseline for comparison to other distributions and can provide valuable insights into the behavior of a system or process.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
14
Views
1K
Replies
0
Views
326
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
970
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
316
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
622
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
843
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
909
Back
Top