sensor networks are an exciting new kind of computer system. Consisting of a
large number of tiny, cheap computational devices physically distributed in an
environment, they gather and process data about the envi
本研究提出了一种新的确定性逼近方法 ——“期望传播”，它将两种先前的技术统一起来，即卡尔曼滤波器的扩展和贝叶斯网络中一种置信传播方法的扩展；试图恢复一个近似分布，其 KL 散度接近真实分布，并且 Experiments with Gaussian mixture models show Expectation Propagation to be convincingly better than methods with similar computational cost: Laplace's method, variational Bayes, and Monte Carlo。而期望传播还提供了一种高效的算法，用于训练贝叶斯点机分类器。