Editing Computational intelligence (section)

=== Bayesian networks ===
In complex application domains, Bayesian networks provide a means to efficiently store and evaluate uncertain knowledge. A Bayesian network is a [[Graphical model|probabilistic graphical model]] that represents a set of random variables and their conditional dependencies by a [[directed acyclic graph]]. The probabilistic representation makes it easy to draw conclusions based on new information. In addition, Bayesian networks are well suited for learning from data.<ref name=":5" /> Their wide range of applications includes medical diagnostics, risk management, information retrieval, and text analysis, e.g. for spam filters. Their wide range of applications includes medical diagnostics, risk management, information retrieval, text analysis, e.g. for spam filters, credit rating of companies, and the operation of complex industrial processes.<ref>{{Cite book |last1=Pourret |first1=Olivier |title=Bayesian networks: a practical guide to applications |last2=Naïm |first2=Patrick |last3=Marcot |first3=Bruce Gregory |date=2008 |publisher=J. Wiley |isbn=978-0-470-06030-8 |series=Statistics in practice |location=Chichester (GB)}}</ref>