e/Variational Bayesian methods

Information
has gloss	eng: Variational Bayesian methods, also called ensemble learning, are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They can be used to lower bound the marginal likelihood (i.e. "evidence") of several models with a view to performing model selection, and often provide an analytical approximation to the parameter posterior probability which is useful for prediction. It is an alternative to Monte Carlo sampling methods for making use of a posterior distribution that is difficult to sample from directly.
lexicalization	eng: Variational Bayesian methods
instance of	e/Bayesian inference