Fine-Tuning the Conditional Probability Distribution Tables
Explore methods to fine-tune conditional probability distribution tables within Bayesian networks. Understand how to identify and correct discrepancies in learned probabilities by manually updating CPDs with domain knowledge. Learn coding techniques to adjust CPDs, improving model accuracy and reliability when working with limited or imperfect data.
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. ...