Content deleted Content added
No edit summary |
m rv poor quality text. Not relevant to the specific term defined here |
||
Line 1:
In [[probability theory]], [[statistics]], and [[machine learning]], a '''graphical model (GM)''' is a graph that represents [[statistical independence|independencies]] among [[random variable]]s by a [[graph (mathematics)|graph]] in which each node is a random variable, and the missing edges between the nodes represent conditional independencies.
Two common types of GMs correspond to graphs with directed and undirected edges. If the network structure of the model is a [[directed acyclic graph]] (DAG), the GM represents a factorization of the joint [[probability]] of all random variables. More precisely, if the events are
| |||