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：仿真推理（SBI）持续探索更具表达力的算法，以便从含噪数据中精确推断复杂模型的参数。我们提出面向神经后验估计的一致性模型（CMPE），这是一种新的自由形式条件采样器，结合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中：其核心在于蒸馏连续概率流，并通过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理。实验评估表明，CMPE不仅在三个高难度低维问题上超越现有最优算法，还在高维贝叶斯去噪实验及对计算密集的肿瘤球多尺度生长模型估计中展现出竞争性性能。