What does a hybrid Bayesian network contain?

A hybrid Bayesian network contains both a discrete and continuous variables.

A hybrid Bayesian network combines elements of both directed and undirected graphical models to represent probabilistic relationships among variables. It typically contains:

1. Directed Acyclic Graph (DAG): This represents conditional dependencies among variables through directed edges. Each node in the graph represents a random variable, and the edges denote direct probabilistic influences.
2. Undirected Graphical Model: This represents the interactions among variables through undirected edges. It’s often used to capture complex dependencies that cannot be fully captured by directed edges alone.

In essence, a hybrid Bayesian network contains both directed and undirected edges, allowing for a more flexible representation of probabilistic relationships, which is particularly useful for modeling complex systems with both causal and associative dependencies.