Bayesian networks are a type of probabilistic graphical model used to represent the dependencies among a set of random variables. They are typically used for modeling complex systems in which a cause-and-effect relationship exists between variables. Bayesian networks consist of nodes, which represent random variables, and edges, which represent the dependencies between the variables. Bayesian networks use Bayesian inference to update probabilities of events based on new evidence or information. They are widely used in a variety of fields, including artificial intelligence, machine learning, bioinformatics, and healthcare. By capturing the probabilistic relationships between variables, Bayesian networks can be used for reasoning under uncertainty and making predictions based on incomplete information.