Overview
Bayesian networks are used to model uncertainty and make predictions based on probabilities. They are based on Bayes' Theorem, which describes the probability of an event based on prior knowledge of conditions related to the event.
Structure
- Nodes: Represent random variables (e.g., 'Rain,' 'Sprinkler,' 'Grass Wet').
- Edges: Represent conditional dependencies (e.g., 'Rain' influences 'Grass Wet').
- Conditional Probability Tables (CPTs): Quantify the strength of the relationships between nodes.
Use Cases
- Medical diagnosis (predicting disease based on symptoms).
- Risk assessment in finance.
- Spam filtering.
- Troubleshooting complex systems.