How do I convert between cdf and pdf?

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To convert the cumulative distribution function (CDF) F(x) = 1 - e^(-αx^β) for x ≥ 0 into the probability density function (PDF), differentiate F(x) with respect to x using the chain rule. For the PDF f(x) = (1 + α)/2 for -1 ≤ x ≤ 1, integrate f(x) over the interval to find the corresponding CDF, ensuring that F(1) equals 1. The integration process should be straightforward since f(x) is constant within the specified range. Specific values of α may be necessary to satisfy the conditions of the CDF.
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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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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 1- e^{-\alpha x^\beta} rather than 1- e^{-\alpha}x^\beta? If so, let u= \alpha x^\beta 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: F(x)= \int_{1}^x (1+\alpha)/2 dt for -1\le x\le 1. That should be easy. Of course, F(1) must be 1. That will require that \alpha 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
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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