论文标题
统一平滑以处理贝叶斯网络中不一致的证据和统一传播的不一致证据以更快地推断
Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference
论文作者
论文摘要
我们提出了统一平滑（US），以解决贝叶斯网络模型与新看不见的观察之间的矛盾。我们表明，与我们一起使用接线树算法的预测准确性与拉普拉斯平滑的准确性相当。此外，在应用中，利用了数据结构的稀疏性，在记忆使用方面，美国的表现优于拉普拉斯平滑。此外，我们详细说明了如何避免在接线树算法中的消息传递方案中必须执行的冗余计算，我们称之为统一传播（UP）。实验结果表明，在连接树算法的Lauritzen Spigelhalter消息传递方案的顶部开发始终更快。
We propose Unity Smoothing (US) for handling inconsistencies between a Bayesian network model and new unseen observations. We show that prediction accuracy, using the junction tree algorithm with US is comparable to that of Laplace smoothing. Moreover, in applications were sparsity of the data structures is utilized, US outperforms Laplace smoothing in terms of memory usage. Furthermore, we detail how to avoid redundant calculations that must otherwise be performed during the message passing scheme in the junction tree algorithm which we refer to as Unity Propagation (UP). Experimental results shows that it is always faster to exploit UP on top of the Lauritzen-Spigelhalter message passing scheme for the junction tree algorithm.
