A Derivation of P.D.F. from distribution function

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The discussion revolves around the derivation of the probability density function (PDF) from the distribution function. The author initially presents the cumulative distribution function (CDF) and seeks clarification on proving the relationship between the CDF and the PDF. The key computation involves demonstrating that the PDF can be expressed as a function of the CDF and its derivatives. Ultimately, the author confirms understanding of the computations, indicating that the question has been resolved. The thread highlights the mathematical relationship between the CDF and PDF in probability theory.
WMDhamnekar
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If ## F_{X_k}(x) = p(X \leq x) = \displaystyle\sum_{j=k}^n \binom{n}{j} F^j(x)(1-F(x))^{n-j}, -\infty
< x < \infty ## then how to prove ##f_{X_k} (x) =\frac{n!}{(k-1)!(n-k)!}f(x) F^{k-1}(x)(1-F(x))^{n-k}##
Author computed ##f_{X_k}(x)## as follows but I don't understand it. Would any member explain me the following computations?
1655626767982.png
 
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WMDhamnekar said:
Summary: If ## F_{X_k}(x) = p(X \leq x) = \displaystyle\sum_{j=k}^n \binom{n}{j} F^j(x)(1-F(x))^{n-j}, -\infty
< x < \infty ## then how to prove ##f_{X_k} (x) =\frac{n!}{(k-1)!(n-k)!}f(x) F^{k-1}(x)(1-F(x))^{n-k}##

Author computed ##f_{X_k}(x)## as follows but I don't understand it. Would any member explain me the following computations?
1655659126417.png
I tag this question as "SOLVED". I understood all the computations. Thanks.
 
Last edited:
Greetings, I am studying probability theory [non-measure theory] from a textbook. I stumbled to the topic stating that Cauchy Distribution has no moments. It was not proved, and I tried working it via direct calculation of the improper integral of E[X^n] for the case n=1. Anyhow, I wanted to generalize this without success. I stumbled upon this thread here: https://www.physicsforums.com/threads/how-to-prove-the-cauchy-distribution-has-no-moments.992416/ I really enjoyed the proof...

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