Basic Structure of Bayesian Networks
A Bayesian network consists of:
- Nodes: Each node represents a random variable, which can be discrete or continuous.
- Edges: Directed edges (arrows) between nodes represent conditional dependencies.
For example, if node A influences node B, there would be a directed edge from A to B, indicating that B is conditionally dependent on A.
Understanding Bayesian Networks: Modeling Probabilistic Relationships Between Variables
Bayesian networks, also known as belief networks or Bayesian belief networks (BBNs), are powerful tools for representing and reasoning about uncertain knowledge. These networks use a graphical structure to encode probabilistic relationships among variables, making them invaluable in fields such as artificial intelligence, bioinformatics, and decision analysis.
This article delves into how Bayesian networks model probabilistic relationships between variables, covering their structure, conditional independence, joint probability distribution, inference, learning, and applications.
Table of Content
- Basic Structure of Bayesian Networks
- Conditional Independence
- Joint Probability Distribution
- Inference in Bayesian Networks
- Learning Bayesian Networks
- Interview Question: “How Do Bayesian Networks Model Probabilistic Relationships Between Variables?”