# Graphical Models

## Bayesian Networks

These are directed graphs, where there is directed edge between two variables which shows cause-effect relation. 

These are modeled using the conditional probability mostly. 

![Conditional Parameterization of Bayesian Graph](https://1877261540-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LFDuA0A2VRqmT31Blrq%2F-M8_3YrcQfCVqb_qfaW4%2F-M8hzKvqFomJyJVk90r5%2Fimage.png?alt=media\&token=053bdcd6-78ae-41d1-8d34-42a145ae88d3)

## Markov Networks

These are undirected graphs. Which doesn't necessarily shows cause-effect but rather the connection between variables. 

Using conditional probability in case of *Undirected Graphical Models* seems erratic because there is no direction and hence no natural conditioning.

In the case of Markov Models, we want to capture the **affinity** between connected random variables.