In the previous chapter, we saw how we can represent a joint probability distribution (JPD) using a directed graph and a set of conditional probability distributions (CPDs). However, it's not always possible to capture the independencies of a distribution using a Bayesian model. In this chapter, we will introduce undirected models, also known as Markov networks. We generally use Markov networks when we can't naturally define directionality in the interaction between random variables.
In this chapter, we will cover: