Simulation-based inference (SBI) is constantly in search of more expressive algorithms for accurately inferring the parameters of complex models from noisy data. We present consistency models for neural posterior estimation (CMPE), a new free-form conditional sampler for scalable, fast, and amortized SBI with generative neural networks. CMPE combines the advantages of normalizing flows and flow matching methods into a single generative architecture: It essentially distills a continuous probability flow and enables rapid few-shot inference with an unconstrained architecture that can be tailored to the structure of the estimation problem. Our empirical evaluation demonstrates that CMPE not only outperforms current state-of-the-art algorithms on three hard low-dimensional problems but also achieves competitive performance in a high-dimensional Bayesian denoising experiment and in estimating a computationally demanding multi-scale model of tumor spheroid growth.

翻译：仿真推断（SBI）持续追求更具表达力的算法，以从含噪数据中准确推断复杂模型的参数。我们提出了用于神经后验估计的一致性模型（CMPE），这是一种新型自由形式条件采样器，能够通过生成神经网络实现可扩展、快速且摊销的SBI。CMPE将归一化流与流匹配方法的优势融合至单一生成架构：本质上，它蒸馏了连续概率流，并利用可针对估计问题结构定制的无约束架构，实现快速少样本推断。我们的实证评估表明，CMPE不仅在三个高难度低维问题上优于当前最先进算法，还在高维贝叶斯去噪实验以及估算计算密集型肿瘤球体生长多尺度模型中展现出竞争性性能。