A Bayesian network, also known as a belief network or probabilistic directed acyclic graphical model, is a graphical representation of probabilistic relationships among a set of variables. These networks are widely used in fields such as artificial intelligence, machine learning, and statistics for modeling complex systems and making predictions based on uncertain information. In a Bayesian network, nodes represent variables, and directed edges between nodes represent probabilistic dependencies between variables. The network encodes a joint probability distribution over all variables, allowing for efficient inference and reasoning about the relationships between variables. Bayesian networks have applications in a wide range of fields, including healthcare, finance, biology, and engineering. They are used for tasks such as diagnosis, prediction, anomaly detection, decision-making, and risk assessment. Bayesian networks are particularly well-suited for tasks that involve uncertainty and incomplete information, as they can incorporate prior knowledge and update beliefs based on new evidence.