講演名 2006-12-05
On Variational Message Passing and Its Relation to Other Message Passing Inference Algorithms
,
PDFダウンロードページ PDFダウンロードページへ
抄録(和)
抄録(英) Variational methods are frequently used for performing inference in graphical models. The sum-product algorithm is often intractable for systems with continuous variables, and variational methods are then an interesting alternative; moreover, variational methods are guaranteed to convergence. On the other hand, it is well known that for discrete variables, the sum-product algorithm usually leads to better results than variational methods (since the Bethe approximation to the Gibbs free energy is more accurate than the mean field approximation); however, the sum-product algorithm is not guaranteed to convergence on cyclic graphs. Those considerations naturally lead to the following questions: Can one perhaps mix both approaches in one algorithm in order to combine their strengths? Which of those combinations lead to variational methods, and hence, are guaranteed to converge? Can such combinations be derived directly from a factor graph of the system at hand by mechanically applying message computation rules, in this way. bypassing error-prone variational calculus? In this note, an answer to those question is outlined. The key idea is to formulate variational methods as message passing algorithms operating on factor graphs, elaborating on earlier work by Beal et al., Winn et al. and Xing et al. In this note, it is also shown that the variational message passing algorithm is strongly related to the message passing formulation of expectation maximization (EM). It is demonstrated how variational methods can be combined with various other message passing algorithms, e.g., Kalman filters and smoothers, iterated conditional modes, EM, gradient methods, and particle filters. Some of those combinations have been explored in the literature, others seem to be new. Generic message computation rules for such combinations are formulated.
キーワード(和)
キーワード(英)
資料番号 NC2006-84
発行日

研究会情報
研究会 NC
開催期間 2006/11/28(から1日開催)
開催地(和)
開催地(英)
テーマ(和)
テーマ(英)
委員長氏名(和)
委員長氏名(英)
副委員長氏名(和)
副委員長氏名(英)
幹事氏名(和)
幹事氏名(英)
幹事補佐氏名(和)
幹事補佐氏名(英)

講演論文情報詳細
申込み研究会 Neurocomputing (NC)
本文の言語 ENG
タイトル(和)
サブタイトル(和)
タイトル(英) On Variational Message Passing and Its Relation to Other Message Passing Inference Algorithms
サブタイトル(和)
キーワード(1)(和/英)
第 1 著者 氏名(和/英) / Justin Dauwels
第 1 著者 所属(和/英)
RIKEN Brain Science Institute
発表年月日 2006-12-05
資料番号 NC2006-84
巻番号(vol) vol.106
号番号(no) 407
ページ範囲 pp.-
ページ数 6
発行日