Bayesian Networks

A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. For example, a Bayesian network can be used to calculate the probability of a patient having a specific disease, given the absence or presence of certain symptoms, if the probabilistic independencies between symptoms and disease as encoded by the graph hold. The term "Bayesian networks" was coined by Pearl (1985) to emphasize three aspects: The often subjective nature of the input informationThe reliance on Bayes's conditioning as the basis for updating informationThe distinction between causal and evidential modes of reasoning, which underscores Thomas Bayes's paper of 1763.
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Networks based on Bayes' theorem, on the inference of probability distributions from data sets.
See also, probabilistic and generalized regression neural networks.