belief network theory

Belief network theory, also known as Bayesian networks, is a way to represent and analyze how different pieces of information or beliefs are connected and influence each other. It uses a graphical model where nodes represent variables or concepts, and links show relationships or dependencies. By understanding these connections, the model can help update beliefs or predictions when new information is available, making it useful for reasoning under uncertainty. This approach is widely used in fields like decision-making, diagnostics, and artificial intelligence to better understand complex systems and improve predictions.