Bayesian Network Basics

Bayesian networks are a powerful tool for representing and reasoning with probabilistic relationships between variables. They are widely used in various fields such as artificial intelligence, machine learning, and statistical inference.

Key Concepts

Nodes: Each node in a Bayesian network represents a variable.
Edges: Edges represent conditional dependencies between variables.
Conditional Probability Tables (CPTs): CPTs specify the probability distribution of a variable given the values of its parents.

Types of Bayesian Networks

Directed Acyclic Graph (DAG): This is the most common type of Bayesian network, where the edges represent direct influences.
Undirected Graph: Also known as Markov random fields, these networks represent pairwise dependencies between variables.

Applications

Medical Diagnosis: Bayesian networks can be used to diagnose diseases by analyzing symptoms and known disease relationships.
Financial Modeling: They can help in predicting market trends and investment risks.
Natural Language Processing: Bayesian networks can be used for tasks like sentiment analysis and language generation.

For more information on Bayesian networks, you can read our detailed guide on Bayesian Network Tutorial.