Number of Synthetic Nodes

The size of CPD tables can become quite large due to the numerous parent nodes, their potential states, and the child node's possible states. This rapid increase in size often results in computational challenges when performing inference and learning. However, synthetic nodes come to the rescue by simplifying these calculations through expert-driven combinational rules based on parent node values. By effectively mitigating the curse of dimensionality, synthetic nodes significantly enhance the usability, efficiency, and overall performance of Bayesian networks, making them indispensable tools for managing complex probabilistic models.

In this lesson, we want to answer the question of how many synthetic nodes does a Bayesian network need.

The answer depends on the structure of the network. But we can explore a simple example to understand how to simplify a structure thanks to synthesized nodes.

Using logical rules in Bayesian networks

Now, let's dig into the process of constructing synthetic nodes using logical rules. The Bayesian networks can be effortlessly transformed into logic trees, and this is very useful for implementing synthetic nodes.

For example let's suppose we have this network with nodes A, B, C.