论文标题
在模块化的贝叶斯推理中切割反馈的一般框架
A General Framework for Cutting Feedback within Modularised Bayesian Inference
论文作者
论文摘要
标准的贝叶斯推理可以构建结合各种来源信息的模型，但是如果模型的组件被弄清楚，则该推断可能不会可靠。切割推断是一种特定类型的模块化贝叶斯推断，是将模型分成模块并从可疑模块中切断的反馈的替代方法。先前的研究集中在两个模块的情况下，但是“模块”的更一般的定义尚不清楚。我们提出了“模块”的正式定义，并讨论其属性。我们制定用于识别模块的方法；确定模块的顺序；并构建应在任意定向的无环形结构中用于切割推理的切割分布。我们通过证明它不仅削减了反馈，而且是满足Kullback-Leibler Divergence的关节分布的最佳近似值来证明切割分布的合理性。我们还通过顺序分裂技术将两种模块案例的推断扩展到了一般的多模块情况，并通过说明性应用证明了这一点。
Standard Bayesian inference can build models that combine information from various sources, but this inference may not be reliable if components of a model are misspecified. Cut inference, as a particular type of modularized Bayesian inference, is an alternative which splits a model into modules and cuts the feedback from the suspect module. Previous studies have focused on a two-module case, but a more general definition of a "module" remains unclear. We present a formal definition of a "module" and discuss its properties. We formulate methods for identifying modules; determining the order of modules; and building the cut distribution that should be used for cut inference within an arbitrary directed acyclic graph structure. We justify the cut distribution by showing that it not only cuts the feedback but also is the best approximation satisfying this condition to the joint distribution in the Kullback-Leibler divergence. We also extend cut inference for the two-module case to a general multiple-module case via a sequential splitting technique and demonstrate this via illustrative applications.
