BriefGPT.xyz
Jun, 2015
Beta Bernoulli 过程的随机变分算法实证研究
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
HTML
PDF
Amar Shah, David A. Knowles, Zoubin Ghahramani
TL;DR
研究使用SVI在稀疏潜在因子模型（尤其是BPFA）中的性能，发现使用Gibbs采样维护特定后验依赖关系非常有效，但在BPFA中不同的后验依赖关系与LDA不同，并且模拟内局部变量依赖性的近似方法表现最佳。
Abstract
stochastic variational inference
(SVI) is emerging as the most promising candidate for scaling inference in
bayesian probabilistic models
to large datasets. However, the performance of these methods has been asse
→