Bayesian Network

A Bayesian network is a probabilistic model that represents variables as nodes and conditional dependencies as directed connections between them. The graph describes how evidence about one factor changes the likelihood of others, making the approach useful when information is incomplete or uncertain. For example, a diagnostic network might connect symptoms, test results, and possible conditions, then update probabilities when new evidence arrives. Bayesian networks can support prediction, diagnosis, risk analysis, and causal reasoning when their assumptions are justified. Their quality depends on the graph structure and probability estimates, which may be learned from data, supplied by experts, or built through a combination of both.