用单点模拟训练模型，大幅提升层次化推断效率
Tokenised Flow Matching for Hierarchical Simulation Based Inference
- 通过似然分解，仅需单点模拟即可训练模型
- 在传染病与流体动力学模型中实现校准后验且降低计算成本
- 适合需要高效层次推断的科研与工程场景
模拟器评估成本是模拟基础推断（SBI）的关键瓶颈。在具有共享全局参数和可交换站点级参数与观测值的层次结构中，可利用此特性提升模拟效率。现有层次化SBI方法虽对后验进行因子分解，但仍需对每个训练样本模拟多个站点；我们改用似然因子分解（LF），从单站点模拟中训练。在LF采样中，学习每个站点的神经模拟器代理，再合成多站点观测以摊销完整层次后验的推断。基于此，我们提出用于后验估计的分块流匹配方法（TFMPE），支持函数型观测的似然因子分解。为实现系统评估，我们引入一个层次化SBI基准。在该基准及真实感染疾病与计算流体动力学模型上验证了TFMPE，结果表明其能获得校准良好的后验分布，同时显著降低计算开销。
原文摘要 · Abstract (English)
The cost of simulator evaluations is a key practical bottleneck for Simulation Based Inference (SBI). In hierarchical settings with shared global parameters and exchangeable site-level parameters and observations, this structure can be exploited to improve simulation efficiency. Existing hierarchical SBI approaches factorise the posterior yet still simulate across multiple sites per training sample; We instead explore likelihood factorisation (LF) to train from single-site simulations. In LF sampling we learn a per-site neural surrogate of the simulator and then assemble synthetic multi-site observations to amortise inference for the full hierarchical posterior. Building on this, we propose Tokenised Flow Matching for Posterior Estimation (TFMPE), a tokenised flow matching approach that supports function-valued observations through likelihood factorisation. To enable systematic evaluation, we introduce a benchmark for hierarchical SBI. We validate TFMPE on this benchmark and on realistic infectious disease and computational fluid dynamics models, finding well-calibrated posteriors while reducing computational cost.
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