Bayesian Networks

Bayesian networks are graphical models that represent the probabilistic relationships among a set of variables. They consist of nodes, which represent the variables, and directed edges, which indicate the dependencies between them. Each node is associated with a probability distribution that quantifies the uncertainty of the variable given its parent nodes. Commonly used in machine learning and data science, Bayesian networks are effective for reasoning under uncertainty, making predictions, and inferring hidden variables. They find applications in various fields, including medical diagnosis, risk management, and decision support systems.