论文标题
可扩展的半模块化推断，具有变异元元素
Scalable Semi-Modular Inference with Variational Meta-Posteriors
论文作者
论文摘要
切割后和相关的半模块化推断是用于模块化贝叶斯证据组合的通用贝叶斯方法。分析在关节后分布的模块化亚模块上分解。单独的模型详细说明可能很难修复多模型模型中的模型 - 密西西比性，并且切割后部和SMI为此提供了一种方式。从错误指定的模块中输入分析的信息由与学习率有关的影响参数$η$控制。本文包含两种实质性的新方法。首先，我们提供了近似剪切和SMI后代的变异方法，这些方法适合于证据组合的推论目标。我们使用归一化流量以准确的近似和端到端训练来参数变异后期。其次，我们表明，使用新的变分元元素对具有多次切割模型的分析是可行的。这近似于使用一组变异参数，由$η$索引的SMI后代家族。
The Cut posterior and related Semi-Modular Inference are Generalised Bayes methods for Modular Bayesian evidence combination. Analysis is broken up over modular sub-models of the joint posterior distribution. Model-misspecification in multi-modular models can be hard to fix by model elaboration alone and the Cut posterior and SMI offer a way round this. Information entering the analysis from misspecified modules is controlled by an influence parameter $η$ related to the learning rate. This paper contains two substantial new methods. First, we give variational methods for approximating the Cut and SMI posteriors which are adapted to the inferential goals of evidence combination. We parameterise a family of variational posteriors using a Normalising Flow for accurate approximation and end-to-end training. Secondly, we show that analysis of models with multiple cuts is feasible using a new Variational Meta-Posterior. This approximates a family of SMI posteriors indexed by $η$ using a single set of variational parameters.
