/explore
|
You are here 
|
preshing.com | ||
| | | | |
www.randomservices.org
[AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight. |
|
| | | | | ||
| | | | |
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? |
|
| | | | | ||
| | | | |
blog.orhun.dev
Let's investigate how to generate random numbers without external dependencies in Rust. |
|
| | | | | ||
| | | | |
robr.dev
Overview I wanted to build a self-sufficient portable lab network consisting entirely of Raspberries Pi. The lab network should be able to access the internet if there's wifi available. And it should be easy to connect an external computer to the network and access any of the Raspberries Pi over the lab network. Basically I'd like to be able to run the whole thing on battery power and use it with or without upstream internet. |
|
| | | |||
