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Processes related to functional decomposition are prevalent throughout the fields of [[knowledge representation]] and [[machine learning]]. Hierarchical model induction techniques such as [[Logic circuit minimization]], [[decision trees]], [[grammatical inference]], [[hierarchical clustering]], and [[quadtree decomposition]] are all examples of function decomposition. A review of other applications and function decomposition can be found in {{Harvtxt|Zupan|Bohanec|Bratko|Demšar|1997}}, which also presents methods based on [[information theory]] and [[graph theory]].
Many [[statistical inference]] methods can be thought of as implementing a function decomposition process in the presence of noise; that is, where functional dependencies are only expected to hold ''approximately''. Among such models are [[mixture models]] and the recently popular methods referred to as "causal decompositions" or [[Bayesian networks]].
===Database theory===
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