arXiv cs.MA· Junyao Zhang, Jinglai Li, Junqi Tang·· 12 小时前AI 评分22
为基于智能体模型的仿真推断适配神经后验估计器
Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models
AI 导读
研究提出 SNPE-TTT 框架，通过测试时训练将预训练的条件归一化流适配到新的仿真推断任务，包含全参数适配、低秩适配及两种由目标任务梯度识别的更新空间共四种策略。在似然可解与不可解的经济仿真模型上评测显示，该框架能用有限的目标仿真预算完成适配，部分设置下降低目标阶段计算成本，但后验精度并无一致优势。
正文
Abstract:Agent-based models (ABMs) are widely used in economics and social sciences. They represent heterogeneous personal behaviors and their interactions by using simulations, whereas their likelihoods are often intractable for parameter estimations. Therefore, most traditional statistical inference techniques are hindered. Driven by recent advances in probabilistic machine learning, recent works have applied neural networks to achieve likelihood-free inference in large-scale ABMs. Specifically, Neural Posterior Estimation using normalizing flows provide amortized Bayesian inference from simulations, yet a pre-trained estimator may become inaccurate when the target task changes. In this work, we investigate the practicality of adapting a pre-trained conditional normalizing flow through a unified sequential neural posterior estimation test-time training (SNPE-TTT) framework. The framework includes four adaptation strategies: full-parameter adaptation, low-rank adaptation, and two update spaces identified from target-task gradients. We evaluate all four methods across economic simulation models with both tractable and intractable likelihoods. These benchmarks demonstrate that the sequential neural posterior estimation test-time training framework can adapt pre-trained posterior estimators to related inference tasks using a limited budget of target simulations. Posterior recovery and computational benefits depend on the task and adaptation strategy, with lower target-stage cost in some settings but no consistently superior posterior accuracy.
| Subjects: | Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2601.07413 [cs.LG] |
| (or arXiv:2601.07413v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.07413 arXiv-issued DOI via DataCite |
Submission history
From: Junqi (Billy) Tang [view email]
[v1]
Mon, 12 Jan 2026 10:55:01 UTC (594 KB)
[v2]
Mon, 26 Oct 2026 13:46:59 UTC (1,975 KB)
来源：arXiv cs.MA · arxiv.org