论文标题
半模块化推断：通过降低组件的影响，在多模型模型中增强了学习
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
论文作者
论文摘要
当生成模型被指定时，贝叶斯统计推断将失去预测性最优性。 在现有的基于贝叶斯推理的基于一致损失的概括中工作，我们显示现有的模块化/切割模型推断是连贯的，并写下了一个新的半模量推理（SMI）方案的新家族，并由影响参数索引，贝叶斯推理和切割模型作为特殊情况。我们给出一个元学习标准和估计程序，以选择推理方案。当没有错误的指定时，这将返回贝叶斯推断。 该框架自然适用于多模型模型。切割模型推理允许有针对性的信息流从明确指定的模块到误指定的模块，但反之亦然。现有的替代功率后验方法可对信息流进行可调但无方向的控制，从而改善了某些设置的预测。相比之下，SMI允许模块之间的可调和定向信息流。 我们说明了有关文献中两个标准测试案例的方法和一个激励的考古数据集。
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.
