Fine-Tuning the Conditional Probability Distribution Tables

Two ways of training Bayesian networks

Conditional probability distributions are essential components in the architecture of Bayesian networks. Understanding CPDs is crucial, especially in scenarios where expert knowledge is paramount or data is limited. We'll discuss how domain experts can effectively use their insights to manually update CPDs, setting the stage for our deeper exploration of this topic.

The key advantage of Bayesian networks lies in their flexibility to be informed by domain expertise. Unlike purely data-driven models, they allow for the integration of expert insights, especially in situations where data may be scarce, incomplete, or too complex. This aspect enables a more nuanced and contextually informed approach to probabilistic reasoning, which we will explore in depth throughout our lesson. By understanding how expert knowledge can shape and refine them, we'll appreciate the full potential of these tools in various applications.

By structuring our lesson in this way, we aim to provide a comprehensive and clear understanding of BNs, starting from foundational concepts and gradually building towards more complex applications.

Simulation vs. calculation

In the following code, we have created the Bayesian network corresponding to the example of Pieter's disease.