Bayesian Networks
A diagram that maps out cause-and-effect relationships and probabilities (e.g., "If it rains, grass is 90% likely wet").
Definition
Probabilistic graphical models that represent variables as nodes and their conditional dependencies as directed edges in an acyclic graph. Each node carries a probability table describing how it depends on its parents. Using Bayes' theorem, the network updates its beliefs as new evidence arrives, letting you reason from causes to effects or from observed effects back to likely causes.
Why it matters
Bayesian networks shine at reasoning under uncertainty with limited data, where pure deep learning struggles. They are widely used in medical diagnosis, fault detection, and risk modeling because their structure is interpretable and shows exactly which factors drive a prediction.
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