半模ularity推断：通过降温组件影响来增强多模块模型的学习
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
AI总结：
本文提出半模ularity推断方法，通过调节组件影响提升多模块模型的学习效果，针对生成模型的不规范性问题提供改进方案。
摘要：
贝叶斯统计推断在生成模型不规范时会失去预测最优性。在现有的基于损失的通用化贝叶斯推断框架下，我们展示现有模块/切割模型推断是相容的，并推导出一个新的半模块推断（SMI）方案族，通过一个影响参数索引。贝叶斯推断和切割模型是其特殊情形。我们提出一个元学习准则和估计程序来选择推断方案。当不存在不规范时，这会返回贝叶斯推断。该框架自然适用于多模块模型。切割模型推断允许从规范模块到不规范模块的定向信息流，但不反向。现有的另一种幂后验方法提供可调但非定向的信息流控制，某些情况下能提升预测。相比之下，SMI允许可调且定向的模块间信息流。我们通过文献中的两个标准测试案例和一个启发性考古数据集来说明我们的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
