Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with this goal, we present consistency models for posterior estimation (CMPE), a new conditional sampler for SBI that inherits the advantages of recent unconstrained architectures and overcomes their sampling inefficiency at inference time. CMPE essentially distills a continuous probability flow and enables rapid few-shot inference with an unconstrained architecture that can be flexibly tailored to the structure of the estimation problem. We provide hyperparameters and default architectures that support consistency training over a wide range of different dimensions, including low-dimensional ones which are important in SBI workflows but were previously difficult to tackle even with unconditional consistency models. Our empirical evaluation demonstrates that CMPE not only outperforms current state-of-the-art algorithms on hard low-dimensional benchmarks, but also achieves competitive performance with much faster sampling speed on two realistic estimation problems with high data and/or parameter dimensions.

翻译：模拟推断（SBI）领域始终在寻求更具表达力且高效的算法，以准确推断复杂仿真模型的参数。为实现这一目标，我们提出了用于后验估计的一致性模型（CMPE）——一种新型的SBI条件采样器，它继承了近期无约束架构的优势，并克服了其在推断时采样效率低下的问题。CMPE本质上通过提炼连续概率流，使无约束架构能够实现快速少样本推断，并可灵活适应估计问题的结构特征。我们提供了一套超参数与默认架构，支持在包括低维场景在内的广泛维度范围内进行一致性训练；低维问题在SBI工作流中至关重要，但此前即使采用无条件一致性模型也难以有效处理。实证评估表明，CMPE不仅在具有挑战性的低维基准测试中超越了当前最先进的算法，还在两个具有高维数据和/或参数的实际估计问题上，以更快的采样速度取得了具有竞争力的性能表现。