A Bayesian Network is a probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph (DAG). It combines principles from graph theory and probability theory to model uncertainty, causality, and inference in complex systems.
Key Characteristics of Bayesian Networks
Directed Acyclic Graph: Nodes represent variables, and edges indicate conditional dependencies.
Conditional Probability Tables (CPTs): Each node contains a table defining probabilities based on parent variables.
Inference Capabilities: Enables prediction or diagnosis by updating beliefs when new evidence is introduced.
Causal Modeling: Supports reasoning about cause and effect relationships.
Explainability: Transparent structure makes it easier to understand decision logic.
Applications of Bayesian Networks
Medical Diagnosis: Assesses disease likelihood based on symptoms and test results.
Risk Analysis: Evaluates probabilities of failure in engineering or financial systems.
Natural Language Processing: Aids in parsing, tagging, and understanding linguistic dependencies.
Recommendation Systems: Predicts user preferences based on observed behaviors.
Robotics and AI: Supports decision-making in uncertain environments.
Why Bayesian Networks Matter
Bayesian networks offer a structured and interpretable approach to reasoning under uncertainty. Their graphical nature and probabilistic foundations make them powerful tools for domains where transparency, inference, and causality are essential.
