Editing Expectation–maximization algorithm (section)

== Relation to variational Bayes methods ==
EM is a partially non-Bayesian, maximum likelihood method. Its final result gives a [[probability distribution]] over the latent variables (in the Bayesian style) together with a point estimate for ''θ'' (either a [[maximum likelihood estimation|maximum likelihood estimate]] or a posterior mode). A fully Bayesian version of this may be wanted, giving a probability distribution over ''θ'' and the latent variables. The Bayesian approach to inference is simply to treat ''θ'' as another latent variable. In this paradigm, the distinction between the E and M steps disappears. If using the factorized Q approximation as described above ([[variational Bayes]]), solving can iterate over each latent variable (now including ''θ'') and optimize them one at a time. Now, ''k'' steps per iteration are needed, where ''k'' is the number of latent variables. For [[graphical models]] this is easy to do as each variable's new ''Q'' depends only on its [[Markov blanket]], so local [[Message passing (disambiguation)|message passing]] can be used for efficient inference.