/explore
|
You are here 
|
www.jeremykun.com | ||
| | | | |
grigory.github.io
Collection of interesting papers on algorithms for big data from 2016. |
|
| | | | | ||
| | | | |
gowers.wordpress.com
Here is a simple but important fact about bipartite graphs. Let $latex G$ be a bipartite graph with (finite) vertex sets $latex X$ and $latex Y$ and edge density $latex \alpha$ (meaning that the number of edges is $latex \alpha |X||Y|$). Now choose $latex (x_1,x_2)$ uniformly at random from $latex X^2$ and $latex (y_1,y_2)$ uniformly |
|
| | | | | ||
| | | | |
www.johnnylogic.org
[AI summary] A technical tutorial on network science basics, mathematical representations, analysis methods, and real-world applications presented by a senior data scientist. |
|
| | | | | ||
| | | | |
mkatkov.wordpress.com
For probability space $latex (\Omega, \mathcal{F}, \mathbb{P})$ with $latex A \in \mathcal{F}$ the indicator random variable $latex {\bf 1}_A : \Omega \rightarrow \mathbb{R} = \left\{ \begin{array}{cc} 1, & \omega \in A \\ 0, & \omega \notin A \end{array} \right.$ Than expected value of the indicator variable is the probability of the event $latex \omega \in... |
|
| | | |||
