Bayesian networks, also known as belief networks, are a directed acyclic graph (DAG) model that represents a set of variables and their conditional dependencies. They are used to encode probabilistic relationships between variables, making them a powerful tool for various applications such as machine learning, data mining, and decision making.
Key Concepts
- Variables: Bayesian networks consist of variables, which can be discrete or continuous.
- Nodes: Each variable is represented by a node in the graph.
- Edges: Edges represent conditional dependencies between variables. An edge from node A to node B indicates that the probability distribution of B depends on the value of A.
Applications
Bayesian networks have a wide range of applications, including:
- Medical Diagnosis: Predicting the presence of diseases based on symptoms and patient data.
- Risk Assessment: Assessing the likelihood of different events, such as natural disasters or financial risks.
- Machine Learning: Building probabilistic models for classification and regression tasks.
Learn More
To delve deeper into the world of Bayesian networks, check out our comprehensive guide on Bayesian Network Basics.
Bayesian Network Example