用测试时训练提升贝叶斯推断中流模型的实时参数估计能力
The Practicality of Normalizing Flow Test-Time Training in Bayesian Inference for Agent-Based Models
- 提出针对分布偏移的测试时微调策略
- 实验证明可实现流模型参数的实时自适应调整
- 适合需要动态响应的经济与社会模拟场景
基于代理的模型（ABMs）因其在描述个体异质决策与交互规则方面的强灵活性，正日益受到经济学和社会科学领域的青睐。本文首次探讨了深度模型如归一化流在ABM参数后验估计中进行测试时训练（TTT）的实用性。我们提出了几种针对分布偏移的实用TTT策略，用于微调归一化流。数值实验表明，这些TTT方案极为有效，能够实现基于流的推理对ABM参数的实时调整。
原文摘要 · Abstract (English)
Agent-Based Models (ABMs) are gaining great popularity in economics and social science because of their strong flexibility to describe the realistic and heterogeneous decisions and interaction rules between individual agents. In this work, we investigate for the first time the practicality of test-time training (TTT) of deep models such as normalizing flows, in the parameters posterior estimations of ABMs. We propose several practical TTT strategies for fine-tuning the normalizing flow against distribution shifts. Our numerical study demonstrates that TTT schemes are remarkably effective, enabling real-time adjustment of flow-based inference for ABM parameters.
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