Bayesian Network

Models and Architectures

A model that maps how events influence each other with probabilities - built for reasoning under uncertainty.

A Bayesian network is a map of causes and effects with probabilities attached: smoking raises the chance of illness, illness raises the chance of certain symptoms. Given evidence - a symptom observed - the network updates the probability of every connected cause.Unlike deep learning, its reasoning is transparent and works with modest data. That keeps it valuable in medical diagnosis, risk analysis, and troubleshooting systems where you must explain the reasoning, not just deliver a verdict.