Editing Machine learning (section)

=== Bayesian networks ===
{{Main|Bayesian network}}
[[Image:SimpleBayesNetNodes.svg|thumb|right|A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.]]

A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic [[graphical model]] that represents a set of [[random variables]] and their [[conditional independence]] with a [[directed acyclic graph]] (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform [[Bayesian inference|inference]] and learning. Bayesian networks that model sequences of variables, like [[speech recognition|speech signals]] or [[peptide sequence|protein sequences]], are called [[dynamic Bayesian network]]s. Generalisations of Bayesian networks that can represent and solve decision problems under uncertainty are called [[influence diagram]]s.