Understanding the CDF & PDF of X's Difference

In summary, the conversation is discussing how to obtain the cdf and pdf of the absolute difference of two iid standard uniform variables X1 and X2. The solution involves using the joint pdf and integrating over the region {(x,y) : |x - y| < t} to obtain a hexagon and using elementary geometry formulas to solve for the cdf and pdf. The exact boundaries of the integrals are still being worked on.
  • #1
roemi
5
0
Hi guys!

Homework Statement



Do you know how to get the cdf and pdf of the absolute difference of two iid standard uniform X1 and X2 : X = |X1 − X2| ?

Homework Equations



Come across http://en.wikipedia.org/wiki/Triang...difference_of_two_standard_uniform_variables" we know that the cdf is 2x - x2 and the pdf is 2 -2x.
How do we get/prove it?Thank you very much!
 
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  • #2


The Attempt at a Solution



Is it solvable using the joint pdf ? Still trying various boundaries for the integral... kinda stuck there... please help ... T_T
[tex] F_{X}(x) = \int \int_{|x_{1}-x_{2}|\leq x} f_{x_{1}}(x_{1}) f_{x_{2}}(x_{2}) dx_{1}dx_{2}[/tex]
 
  • #3


*bump*
 
  • #4


roemi said:

The Attempt at a Solution



Is it solvable using the joint pdf ? Still trying various boundaries for the integral... kinda stuck there... please help ... T_T
[tex] F_{X}(x) = \int \int_{|x_{1}-x_{2}|\leq x} f_{x_{1}}(x_{1}) f_{x_{2}}(x_{2}) dx_{1}dx_{2}[/tex]

Yes, this is the way to do it. I'll use different variables to simplify notation. X is uniform on [0,1], and so is Y.

Fix t. Find P(|X-Y|<t). Draw the region {(x,y) : |x - y| < t}. Intersect it with the unit square [0,1] x [0,1]. The shape of the resulting region depends on t. For the interesting (i.e. nontrivial) values of t, you get a hexagon.

Since f_X(x) and f_Y(y) are constants, the "integration" can be done simply by using elementary geometry formulas (e.g. area of square, area of triangle).
 
  • #5


Thanks for the reply.

Umm... what would be the boundaries/intervals in the first and second integral ?
 
  • #6


*bump*

Anyone interested in solving this problem, please don't hesitate,

still waiting ... :)
 

Related to Understanding the CDF & PDF of X's Difference

1. What is the difference between CDF and PDF?

CDF (Cumulative Distribution Function) is a function that shows the probability of a random variable taking a value less than or equal to a certain value. PDF (Probability Density Function) is a function that describes the probability distribution of a continuous random variable.

2. How do CDF and PDF help in understanding the difference between two random variables?

CDF and PDF help in understanding the difference between two random variables by providing information about the probability of the difference between the two variables taking on a certain value. CDF shows the cumulative probability of the difference being less than or equal to a certain value, while PDF shows the probability density at a specific value of the difference.

3. What is the relationship between CDF and PDF?

CDF and PDF are closely related as CDF is the integral of PDF. In other words, CDF is the cumulative sum of all the probabilities in the PDF. Additionally, the PDF can be obtained by taking the derivative of the CDF.

4. How are CDF and PDF used in statistical analysis?

CDF and PDF are used in statistical analysis to describe the probability distribution of a random variable and to calculate the probabilities of certain values or ranges of values. They are essential in calculating confidence intervals, hypothesis testing, and other statistical techniques.

5. Can CDF and PDF be used for any type of random variable?

Yes, CDF and PDF can be used for any type of random variable, whether discrete or continuous. For discrete random variables, the PDF is replaced by a Probability Mass Function, and the CDF is replaced by a Cumulative Mass Function.

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