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Approximate inference : ウィキペディア英語版
Approximate inference
Approximate inference methods make it possible to learn realistic models from big data by trading off computation time for accuracy, when exact learning and inference are computationally intractable.
==Major methods classes ==

*Variational Bayesian methods
*Expectation propagation
*Markov random fields
*Bayesian networks
*
*Variational message passing
*loopy and generalized belief propagation
〔(【引用サイトリンク】title=Approximate Inference )

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
ウィキペディアで「Approximate inference」の詳細全文を読む



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