Explore >> Select a destination
|
You are here 
|
www.kuniga.me | ||
| | | | |
nelari.us
|
|
| | | | | In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution? | |
| | | | |
www.ethanepperly.com
|
|
| | | | | ||
| | | | |
gregorygundersen.com
|
|
| | | | | Gregory Gundersen is a quantitative researcher in New York. | |
| | | | |
jmanton.wordpress.com
|
|
| | | If $latex Y$ is a $latex \sigma(X)$-measurable random variable then there exists a Borel-measurable function $latex f \colon \mathbb{R} \rightarrow \mathbb{R}$ such that $latex Y = f(X)$. The standard proof of this fact leaves several questions unanswered. This note explains what goes wrong when attempting a "direct" proof. It also explains how the standard proof... | ||
