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 based on Bayes' Theorem, which provides a way to update the probability of a hypothesis as more evidence becomes available. Bayesian Networks are widely used in various fields, including machine learning, bioinformatics, and diagnostic systems, to model complex systems and make predictions based on incomplete information.