How to Convert Between CDF and PDF Functions in Mathematical Equations?

  • Thread starter scot72001
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    Cdf Pdf
CIn summary, the conversation discusses the process of converting a cdf to a pdf and vice versa. The first cdf function is given as F(x) = 1-e^(-αx^β) for x ≥ 0, α>0, β>0, and the corresponding pdf is requested. The second pdf function is given as f(x) = (1+α)/2 for -1 ≤ x ≤ 1, -1 ≤ α ≤ 1, and the corresponding cdf is requested. Differentiating and integrating these functions is discussed, with the use of the chain rule for differentiation and the constant (1+α)/2 for integration.
  • #1
scot72001
5
0
cdf to pdf and vise versa
hi
i'm looking for help when going from a cdf function:
F(x) = { 1- e^-αx^β x ≥ 0, α>0, β>0
{ 0 x < 0

to getting the corresponding pdf

also i am looking to do the opposite(pdf to cdf)
for:
f(x) = { (1 + α)/2 for -1 ≤ x ≤ 1, -1 ≤ α ≤ 1
{ 0 otherwise

i'm unsure as to how to integrate and differentiate these parts.
can you help please

thanks
michael
 
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  • #2
You don't need to post the same question in multiple places.
 
  • #3
sorry i just noticed it said put homework in the homework section after i had posted it in the first location
 
  • #4
As for differentiating, you need to use the chain rule. Integrating that function should be simple since (1 + a)/2 is a constant with respect to X.
 
  • #5
so would the following look ok for the first part?
f(x) = {e^-αx^β } {αβ [x^(β-1) } x ≥ 0, α>0, β>0

and for the second part it should look like
f(x) = { (1 + αx)/2 for -1 ≤ x ≤ 1, -1 ≤ α ≤ 1 to start off. apologies for missing the x as it is crucial. do you think i can still use 1+α as a constant
then the answer would look like
((1 +α)x^2)/4
 

1. What is a CDF and PDF?

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

2. How are CDF and PDF related?

CDF and PDF are related by the fact that the PDF is the derivative of the CDF. This means that the area under the PDF curve between two points is equal to the difference in the CDF values at those two points. In other words, the CDF is the integral of the PDF.

3. When would you use a CDF over a PDF, and vice versa?

A CDF is useful when you want to know the probability of a random variable being less than or equal to a certain value. It is also helpful for calculating percentiles. A PDF is useful when you want to know the probability density (or likelihood) of a specific value occurring for a continuous random variable.

4. How do you convert from CDF to PDF?

To convert from CDF to PDF, you can use the following formula: PDF(x) = d/dx(CDF(x)). This means you take the derivative of the CDF function with respect to x to get the PDF function.

5. Can you convert from PDF to CDF?

Yes, you can convert from PDF to CDF by integrating the PDF function. This means you take the integral of the PDF function with respect to x to get the CDF function.

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