Definition: (law of marginal probability, also called “sum rule”) Let $A$ and $X$ be two arbitrary statements about random variables, such as statements about the presence or absence of an event or about the value of a scalar, vector or matrix. Furthermore, assume a joint probability distribution $p(A,X)$. Then, $p(A)$ is called the marginal probability of $A$ and,
- Wikipedia (2020): "Marginal distribution" ; in: Wikipedia, the free encyclopedia , retrieved on 2020-05-10 ; URL: https://en.wikipedia.org/wiki/Marginal_distribution#Definition .
- Jason Browlee (2019): "A Gentle Introduction to Joint, Marginal, and Conditional Probability" ; in: Machine Learning Mastery , retrieved on 2021-08-01 ; URL: https://machinelearningmastery.com/joint-marginal-and-conditional-probability-for-machine-learning/ .
Metadata: ID: D50 | shortcut: prob-marg | author: JoramSoch | date: 2020-05-10, 20:01.