Approximate inference algorithms for two-layer Bayesian networks

被引：0

作者：

Ng, AY ^{[1
]}

Jordan, MI ^{[1
]}

机构：

[1] Univ Calif Berkeley, Div Comp Sci, Berkeley, CA 94720 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12 | 2000年 / 12卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a class of approximate inference algorithms for graphical models of the QMR-DT type. We give convergence rates for these algorithms and for the Jaakkola and Jordan (1999) algorithm, and verify these theoretical predictions empirically. We also present empirical results on the difficult QMR-DT network problem, obtaining performance of the new algorithms roughly comparable to the Jaakkola and Jordan algorithm.

引用

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页码：533 / 539

页数：7

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