probability - Find expected value using CDF - Cross Validated @styfle - because that's what a PDF is, whenever the CDF is continuous and differentiable You can see this by looking at how you have defined your CDF Differentiating an integral just gives you the integrand when the upper limit is the subject of the differentiation
estimation - What is the proper way to estimate the CDF for a . . . My initial thought was 'I don't think there is an answer to this question ' Similar to what is written in one of the replies The idea of a confidence interval about some smooth curve, and then getting narrower as n increases, seems like a cool idea, though
Derivation and meaning of 1 minus the cumulative distribution? @Sergio thanks for the derivation is the meaning that $1-F (X)$ is just the other 'half' of the CDF? and what is the condition after $:$ saying? it just ensures that the CDF and its 'other half' sum to 1?
Calculating PDF given CDF - Cross Validated I know that the PDF is the first derivative of the CDF for a continuous random variable, and the difference for a discrete random variable However, I would like to know why this is, why are there