Stochastic variational Bayes algorithms have become very popular in the machine learning literature, particularly in the context of nonparametric Bayesian inference. These algorithms replace the true but intractable posterior distribution with the best (in the sense of Kullback-Leibler divergence) member of a tractable family of distributions, using stochastic gradient algorithms to perform the optimization step. stochastic variational Bayes inference implicitly trades off computational speed for accuracy, but the loss of accuracy is highly model (and even dataset) specific. In this paper we carry out an empirical evaluation of this trade off in the context of stochastic blockmodels, which are a widely used class of probabilistic models for network and relational data. Our experiments indicate that, in the context of stochastic blockmodels, relatively large subsamples are required for these algorithms to find accurate approximations of the posterior, and that even then the quality of the approximations provided by stochastic gradient variational algorithms can be highly variable.

翻译：随机变分贝叶斯算法在机器学习领域已变得非常流行，特别是在非参数贝叶斯推断的背景下。这些算法通过使用随机梯度算法执行优化步骤，将真实但难以处理的后验分布替换为可处理分布族中最佳（以Kullback-Leibler散度衡量）的成员。随机变分贝叶斯推断隐性地以计算速度换取精度损失，但精度损失的程度高度依赖于具体模型（甚至数据集）。本文在随机块模型的背景下对此权衡关系进行了实证评估——随机块模型是网络和关系数据建模中广泛使用的一类概率模型。实验表明，在随机块模型场景中，这些算法需要相对较大的子样本才能获得后验分布的精确近似，且即使如此，随机梯度变分算法提供的近似质量仍可能存在显著波动。