论文标题
力矩传播
Moment Propagation
论文作者
论文摘要
我们介绍并发展了近似贝叶斯推断的力矩传播。该方法可以看作是平均场变异贝叶斯的方差校正，倾向于低估后差异。着重于通过两组参数向量描述模型的情况，我们为线性回归，多元正常和概率回归模型开发了力矩传播算法。我们为概率回归模型显示了矩传的阶段在几个基准数据集的经验上表现良好。最后，我们讨论理论差距和未来扩展。在补充材料中，我们从启发式上表明了为什么力矩传播会导致适当的后方差估计，对于线性回归和多元正常模型，我们精确地表明了为什么平均平均值野外变异贝叶在某些时刻低估了某些时刻，并证明我们的矩矩传播算法以恢复了所有临时性的分布，以恢复所有概率的均值，以示出该均值的均值，我们的临时分布均为该概述，以获取该参数性，以供该参数，以获取该参数性，以获取该参数性的参数性，以供该参数构成。平均值和协方差估计。
We introduce and develop moment propagation for approximate Bayesian inference. This method can be viewed as a variance correction for mean field variational Bayes which tends to underestimate posterior variances. Focusing on the case where the model is described by two sets of parameter vectors, we develop moment propagation algorithms for linear regression, multivariate normal, and probit regression models. We show for the probit regression model that moment propagation empirically performs reasonably well for several benchmark datasets. Finally, we discuss theoretical gaps and future extensions. In the supplementary material we show heuristically why moment propagation leads to appropriate posterior variance estimation, for the linear regression and multivariate normal models we show precisely why mean field variational Bayes underestimates certain moments, and prove that our moment propagation algorithm recovers the exact marginal posterior distributions for all parameters, and for probit regression we show that moment propagation provides asymptotically correct posterior means and covariance estimates.
