AI 中文总结
针对基于仿真的推断中模型选择与误设检测难题，提出神经证据估计（NEE）方法，利用归一化流直接学习边际似然，在合成与真实流感疫情数据上验证了其可靠性与实用性。
AI 中文摘要
基于仿真的推断（SBI）的神经技术已在具有复杂仿真模型的科学领域中变得流行。这些方法在模拟数据上训练神经网络以推断未知参数；其可靠性取决于仿真器是否忠实地代表现实。在传染病建模中，编码不同假设的多个模型通常能为观测数据提供合理的解释。当似然不可用时，模型选择是一个难题，许多常见的随机传播模型就是这种情况。我们提出了一种专为SBI设计的模型批评方法——神经证据估计（NEE），它整合了模型选择和模型误设检测。NEE使用归一化流直接从仿真中学习模型的边际似然（即证据），从而避免了有噪声或复杂的蒙特卡洛估计器。通过合成实验，我们证明NEE能可靠地在多个模型中选择正确的数据生成过程，并标记出设定不佳的模型。我们将NEE应用于一个关于多波疫情中流感再感染的竞争机制的真实世界建模问题。这项工作论证了在SBI和传染病建模中进行有原则的模型批评的重要性。
英文摘要
Neural techniques for simulation-based inference (SBI) have become popular in scientific domains featuring complex simulation models. These methods train neural networks on simulated data to infer unknown parameters; their reliability depends on the simulator faithfully representing reality. In infectious disease modeling, multiple models encoding different hypotheses often provide plausible explanations of observed data. Model selection is a difficult problem when likelihoods are unavailable, as is the case with many common stochastic transmission models. We propose a model criticism methodology designed for SBI, Neural Evidence Estimation (NEE), that integrates model selection and model misspecification detection. NEE learns a model's marginal likelihood (a.k.a. evidence) directly from simulations using normalizing flows, thereby avoiding noisy or complex Monte Carlo estimators. Through synthetic experiments, we demonstrate that NEE reliably selects the correct data generating process among multiple models and flags poorly specified models. We apply NEE to a real-world modeling problem concerning competing mechanisms for influenza reinfection in a multiple wave epidemic. This work argues for the importance of principled model criticism in SBI and infectious disease modeling.
Comments37 pages, 7 figures
