 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.See database normalization.