标题： 通过顺序元后验抽样实现实时贝叶斯选举预测中的高效情景分析 显示英文标题

标题： Efficient scenario analysis in real-time Bayesian election forecasting via sequential meta-posterior sampling

摘要： 贝叶斯聚合让选举预测者可以结合多种信息来源，如州级民意调查和经济及政治指标：例如我们与《经济学人》杂志的合作。 然而，实时后验更新、模型检查和沟通带来了实际的方法论挑战。 特别是，敏感性和情景分析有助于追踪预测变化到模型假设，并理解模型行为。 然而，在标准的马尔可夫链蒙特卡洛方法下，即使是对模型的小调整（例如先验、数据、超参数）也需要完全重新拟合，使得这种实时分析在计算上非常昂贵。 为克服这一瓶颈，我们引入了一种元模型策略，并配以顺序采样方案；通过遍历后验元模型，我们能够在不重复重新拟合的情况下实现实时推断和结构化的场景和敏感性分析。 在模型的回测中，我们展示了显著的计算优势并发现了非平凡的敏感性模式。 例如，预测对基于基本面的先验置信度仍然敏感，但对随机游走尺度的敏感性较低；这些有助于澄清民意调查数据与结构性假设的相对影响。 代码可在 https://github.com/geonhee619/SMC-Sense 获取。 显示英文摘要

摘要： Bayesian aggregation lets election forecasters combine diverse sources of information, such as state polls and economic and political indicators: as in our collaboration with The Economist magazine. However, the demands of real-time posterior updating, model checking, and communication introduce practical methodological challenges. In particular, sensitivity and scenario analysis help trace forecast shifts to model assumptions and understand model behavior. Yet, under standard Markov chain Monte Carlo, even small tweaks to the model (e.g., in priors, data, hyperparameters) require full refitting, making such real-time analysis computationally expensive. To overcome the bottleneck, we introduce a meta-modeling strategy paired with a sequential sampling scheme; by traversing posterior meta-models, we enable real-time inference and structured scenario and sensitivity analysis without repeated refitting. In a back-test of the model, we show substantial computational gains and uncover non-trivial sensitivity patterns. For example, forecasts remain responsive to prior confidence in fundamentals-based forecasts, but less so to random walk scale; these help clarify the relative influence of polling data versus structural assumptions. Code is available at https://github.com/geonhee619/SMC-Sense.