A Bayesian Network may contains both
discrete and
continuous variables. This BN is also called Hybrid Bayesian Network. The figure below illustrates an example of a simple Hybrid Bayesian Network.
The discrete variables in the network are depicted as circles and the continuous variables as rectangles.
For any directed graphical model the conditional distribution of each node given its parents nodes must be defined. Many strategies have been proposed in the literature, in order to tackle the problem of representing the conditional distribution of any type of dependency between the nodes in a BN. Each of these strategies are discussed below. From this Bayesian Network example the following types of dependencies between the nodes of the network can be identified.
- Discrete child node with discrete parent node (B --> C).
- Continuous child node with continuous parent node (A --> E).
- Continuous child node with discrete parent node (C --> D).
- Discrete child node with continuous parent node (A --> C).
