A probabilistic graphical model is a graph-based representation which encodes a distribution. It’s used mainly to represent the conditional dependencies between variables, making it easier to understand and analyze the underlying structure of the distribution.
There are two types of graphical models:
- Bayesian Networks (or Directed Graphical Models).
- Markov Networks (a.k.a. Markov Random Fields, or Undirected Graphical Models)
tags: probability-theory - graph-theory