## Introduction

Directed graphical models, popularly known as Bayesian networks, are an important family of probabilistic graphical models. They are a convenience method to express complicated relationships among random variables. Being graphical, Bayesian networks offer a simple way to visualize probabilistic dependencies between variables. Moreover, complex computations such as those required to perform learning and inference can be expressed in terms of graphical operations.

The *undirected* counterparts to Bayesian networks are known as Markov random fields, or simply undirected graphical models.
We cover those separately.