Number of Synthetic Nodes
Explore how synthetic nodes enhance Bayesian networks by simplifying complex conditional probability tables using logical gates such as AND and OR. Learn to reduce computational challenges and improve network efficiency by constructing synthetic nodes based on expert-driven logical rules. This lesson helps you understand the structure-dependent number of synthetic nodes required and how to implement them practically using Python's NumPy functions.
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 ...