belief networks

Belief networks, also known as Bayesian networks, are graphical models that represent a set of variables and their conditional dependencies using directed acyclic graphs. Each node represents a variable, while arrows indicate influence or causation between them. This framework allows for reasoning about uncertain information, enabling predictions and decision-making based on the relationships between variables. For example, in medical diagnosis, a belief network can help determine the likelihood of a disease based on symptoms and patient history, evolving its understanding as new evidence is introduced. Thus, they are valuable tools for managing uncertainty in various fields.