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.

翻译：我们建议统一滑动(US ), 用于处理巴伊西亚网络模型和新的不可见观测之间的不一致之处。 我们表明,使用与美国接合树算法的预测准确性与美国的接合树算法相当。 此外,在应用中,数据结构的宽度被使用,美国在记忆使用方面的表现优于拉比特。 此外,我们详细说明了如何避免在连接树算法(我们称之为Unity propagation (UP))中信息传递方法中必须进行的重复计算。 实验结果显示,在连接树算法的Lauritzen-Spigelhalter信息传递方法之上进行利用总是更快。