标题： 半模块化推理：通过调节组件的影响在多模块模型中增强学习 显示英文标题

标题： Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components

摘要： 贝叶斯统计推断在生成模型被错误指定时会失去预测最优性。 在现有的基于损失的贝叶斯推断推广框架内，我们证明了现有的模块化/截断模型推断是自洽的，并写出了一族由影响参数索引的半模块化推断（SMI）方案，贝叶斯推断和截断模型作为特例。 我们给出一个元学习准则和估计过程来选择推断方案。 当没有错误指定时，这会返回贝叶斯推断。 该框架自然适用于多模块模型。 截断模型推断允许从正确指定的模块到错误指定的模块的信息流，但反之则不允许。 一种现有的替代幂后验方法提供了可调节但非方向的信息流控制，在某些情况下改善预测。 相比之下，SMI允许模块间可调节且方向性的信息流。 我们在文献中的两个标准测试案例和一个有启发性的考古数据集上展示了我们的方法。 显示英文摘要

摘要： Bayesian statistical inference loses predictive optimality when generative models are misspecified. Working within an existing coherent loss-based generalisation of Bayesian inference, we show existing Modular/Cut-model inference is coherent, and write down a new family of Semi-Modular Inference (SMI) schemes, indexed by an influence parameter, with Bayesian inference and Cut-models as special cases. We give a meta-learning criterion and estimation procedure to choose the inference scheme. This returns Bayesian inference when there is no misspecification. The framework applies naturally to Multi-modular models. Cut-model inference allows directed information flow from well-specified modules to misspecified modules, but not vice versa. An existing alternative power posterior method gives tunable but undirected control of information flow, improving prediction in some settings. In contrast, SMI allows tunable and directed information flow between modules. We illustrate our methods on two standard test cases from the literature and a motivating archaeological data set.