A Bayesian Belief Network (BBN) is a graphical model used in Artificial Intelligence to represent probabilistic relationships among a set of variables. It is composed of nodes (representing variables) and directed edges (indicating dependencies between these variables). Each node is associated with a probability distribution, and the network is based on Bayesian inference, allowing it to compute the likelihood of various outcomes given new evidence.
BBNs are widely used in decision-making, diagnostics, and reasoning under uncertainty. They excel in domains like medical diagnosis, natural language processing, and risk analysis, as they efficiently combine prior knowledge with observed data to make predictions.