Probabilistic Inference by means of Cluster Variation Method and Linear Response Theory(Algorithms)
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概要
- 論文の詳細を見る
Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.
- 一般社団法人電子情報通信学会の論文
- 2003-07-01
著者
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Tanaka Kazuyuki
Department Of Applied Information Sciences Graduate School Of Information Sciences Tohoku University
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Tanaka Kazuyuki
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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