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
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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.
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页码:533 / 539
页数:7
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