How do I convert between cdf and pdf?

  • Thread starter scot72001
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In summary, Michael is seeking help with understanding how to go from a cdf function to a corresponding pdf and vice versa for two different functions. He is unsure about how to integrate and differentiate these functions and is looking for assistance.
  • #1
scot72001
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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
scot72001 said:
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
Is that [itex]1- e^{-\alpha x^\beta}[/itex] rather than [itex]1- e^{-\alpha}x^\beta[/itex]? If so, let [itex]u= \alpha x^\beta[/itex] and use the chain rule: df/dx= (df/du)(du/dx).

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
Then integrate: [itex]F(x)= \int_{1}^x (1+\alpha)/2 dt[/itex] for [itex]-1\le x\le 1[/itex]. That should be easy. Of course, F(1) must be 1. That will require that [itex]\alpha[/itex] have a specific value. In fact, since f(x) is a constant, this is a uniform probability and you should be able to do it without integrating.

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

thanks
michael
 

Related to How do I convert between cdf and pdf?

1. What is the difference between CDF and PDF?

CDF (Cumulative Distribution Function) is a function that maps a value to the probability of a random variable being less than or equal to that value. PDF (Probability Density Function) is a function that gives the probability of a random variable falling within a certain range of values. In other words, CDF gives the cumulative probability of a value, while PDF gives the probability density at a specific value.

2. How do you convert CDF to PDF?

To convert CDF to PDF, you can use the following formula: PDF = d/dx(CDF). In other words, you take the derivative of the CDF function to get the PDF function. This is because the derivative of the CDF is equal to the PDF.

3. Can you convert PDF to CDF?

Yes, you can convert PDF to CDF using the following formula: CDF = ∫PDF dx. In other words, you integrate the PDF function to get the CDF function. This is because the integral of the PDF is equal to the CDF.

4. What is the purpose of CDF and PDF in statistics?

CDF and PDF are important tools in statistics for understanding the distribution of a random variable. CDF can help us calculate probabilities for specific values or ranges of values, while PDF helps us understand the relative likelihood of different values occurring.

5. How are CDF and PDF used in real-life applications?

CDF and PDF are used in a variety of real-life applications, such as finance, economics, and engineering. For example, in finance, CDF and PDF can be used to model stock returns or interest rates. In engineering, CDF and PDF can be used to analyze the reliability of a system. In both cases, understanding the distribution of a random variable is crucial for making informed decisions.

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