Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
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以迭代筛选支持贝叶斯建模中的多宇宙分析
- 研究问题
- 贝叶斯建模中的迭代修改与不同建模选择会产生大量候选模型，如何透明且有效地评估和筛选这些模型？
- 方法与思路
- 作者将多宇宙分析与贝叶斯建模工作流结合，在考虑因果约束的前提下，依据预测能力和计算诊断开展迭代筛选。
- 材料陈述的贡献
- 摘要提出适用于不同建模场景的迭代筛选流程，旨在一致地评估多个模型，并用真实数据案例展示缩减候选模型的过程。
- 阅读关注点
- 应核对候选模型生成规则、筛选标准、数据重复使用的处理，以及因果约束如何限制以预测表现为依据的模型选择。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出筛选后推断的具体有效性保证，也未说明案例与计算收益。
原文摘要
When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations throughout the modelling process can motivate changes to an existing model or the consideration of alternative models to ultimately obtain models of sufficient quality for the problem at hand. Additionally, failing to consider alternative models can lead to overconfidence in the predictive or inferential ability of a chosen model. The search for suitable models requires modellers to work with multiple models without jeopardising the validity of their results. Multiverse analysis offers a framework for transparent creation of multiple models at once based on different sensible modelling choices, but the number of candidate models arising in the combination of iterations and possible modelling choices can become overwhelming in practice. Motivated by these challenges, this work proposes iterative filtering for multiverse analysis to support efficient and consistent assessment of multiple models and meaningful filtering towards fewer models of higher quality across different modelling contexts. Given that causal constraints have been taken into account, we show how multiverse analysis can be combined with recommendations from established Bayesian modelling workflows to identify promising candidate models by assessing predictive abilities and, if needed, tending to computational issues. We illustrate our suggested approach in different realistic modelling scenarios using real data examples.