Variational Bayesian Methods
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High Quality Content by WIKIPEDIA articles! Variational Bayesian methods, also called ensemble learning, are a family of techniques for approximating intractable integrals arising in Bayesian statistics and machine learning. They can be used to lower bound the marginal likelihood 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.

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High Quality Content by WIKIPEDIA articles! Variational Bayesian methods, also called ensemble learning, are a family of techniques for approximating intractable integrals arising in Bayesian statistics and machine learning. They can be used to lower bound the marginal likelihood 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.