A Bayesian network is a graphical model that represents a set of variables and their conditional dependencies using directed acyclic graphs. Each node in the graph represents a variable, while the edges indicate the relationships between them. This structure allows for efficient computation of probabilities, making it useful for reasoning under uncertainty. These networks are widely used in various fields, including machine learning, bioinformatics, and artificial intelligence. By applying Bayes' theorem, they can update the probability of a hypothesis as more evidence becomes available, enabling better decision-making based on incomplete or uncertain information.