Variational Bayesian methods: Difference between revisions

Revision as of 12:58, 3 May 2022

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{{Short description|Mathematical methods used in Bayesian inference and machine learning}}

{{For|the method of approximation in quantum mechanics|Variational method (quantum mechanics)}}

{{Bayesian statistics}}

'''Variational Bayesian methods''' are a family of techniques for approximating intractable [[integral]]s arising in [[Bayesian inference]] and [[machine learning]]. They are typically used in complex [[statistical model]]s consisting of observed variables (usually termed "data") as well as unknown [[parameter]]s and [[latent variable]]s, with various sorts of relationships among the three types of [[random variable]]s, as might be described by a [[graphical model]]. As typical in Bayesian inference, the parameters and latent variables are grouped together as "unobserved variables". Variational Bayesian methods are primarily used for two purposes: