Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian 
Inference

Seeger, M; Nickisch, H

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

報告書

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

MPS-Authors

/persons/resource/persons84109

Nickisch, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://arxiv.org/abs/1012.3584
(全文テキスト（全般）)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Seeger, M., & Nickisch, H.(2010). Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-BD46-F

要旨

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opperamp;Winther 05) with covariance decoupling techniques (Wipfamp;Nagarajan 08, Nickischamp;Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.