Averaged Collapsed Variational Bayes Inference

被引:0
|
作者
Ishiguro, Katsuhiko [1 ]
Sato, Issei [2 ]
Ueda, Naonori [1 ]
机构
[1] NTT Corp, NTT Commun Sci Labs, Kyoto 6190237, Japan
[2] Univ Tokyo, Grad Sch Frontier Sci, Tokyo 1130033, Japan
关键词
nonparametric Bayes; collapsed variational Bayes inference; averaged CVB;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents the Averaged CVB (ACVB) inference and offers convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. CVB inferences yield more precise inferences of Bayesian probabilistic models than Variational Bayes (VB) inferences. However, their convergence aspect is fairly unknown and has not been scrutinized. To make CVB more useful, we study their convergence behaviors in a empirical and practical approach. We develop a convergence-guaranteed algorithm for any CVB-based inference called ACVB, which enables automatic convergence detection and frees non-expert practitioners from the difficult and costly manual monitoring of inference processes. In experiments, ACVB inferences are comparable to or better than those of existing inference methods and deterministic, fast, and provide easier convergence detection. These features are especially convenient for practitioners who want precise Bayesian inference with assured convergence.
引用
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页数:29
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