论文标题
SOLBP：二阶环路信念传播，用于不确定的贝叶斯网络中的推断
SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks
论文作者
论文摘要
在二阶不确定的贝叶斯网络中，条件概率仅在分布中已知，即概率上的概率。 Delta方法已应用于扩展确切的一阶推理方法，以通过从贝叶斯网络得出的总和 - 产品网络来传播均值和方差，从而表征了认知不确定性或模型本身的不确定性。另外，已经证明了Polytrees的二阶信仰传播，但没有用于一般的定向无环形结构。在这项工作中，我们将循环信念传播扩展到二阶贝叶斯网络的设置，从而产生二阶循环信念传播（SOLBP）。对于二阶贝叶斯网络，SOLBP生成了与Sum-Propoduct网络生成的推论，同时更有效且可扩展。
In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in the model itself. Alternatively, second-order belief propagation has been demonstrated for polytrees but not for general directed acyclic graph structures. In this work, we extend Loopy Belief Propagation to the setting of second-order Bayesian networks, giving rise to Second-Order Loopy Belief Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates inferences consistent with those generated by sum-product networks, while being more computationally efficient and scalable.
