Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as they tend to require a large number of simulator calls to learn accurate approximations. In contrast, Neural Likelihood Estimation methods can handle multiple observations at inference time after learning from individual observations, but they rely on standard inference methods, such as MCMC or variational inference, which come with certain performance drawbacks. We introduce a new method based on conditional score modeling that enjoys the benefits of both approaches. We model the scores of the (diffused) posterior distributions induced by individual observations, and introduce a way of combining the learned scores to approximately sample from the target posterior distribution. Our approach is sample-efficient, can naturally aggregate multiple observations at inference time, and avoids the drawbacks of standard inference methods.

翻译：针对基于仿真的推断问题，神经后验估计方法在处理基于多个观测条件化的后验分布时存在不足——这类方法通常需要大量仿真器调用才能获得精确近似。相比之下，神经似然估计方法在从单个观测学习后可在推理阶段处理多观测数据，但其依赖马尔可夫链蒙特卡洛或变分推断等标准推断方法，存在特定性能缺陷。本文提出基于条件评分建模的新方法，融合两类方法的优势。我们通过建模单个观测所诱导（扩散）后验分布的评分函数，并设计组合学习评分的方法以近似采样目标后验分布。该方案具有样本高效性，可在推理阶段自然聚合多观测数据，同时规避了标准推断方法的局限性。