High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. In this work, we introduce a simple extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. Like LF2I, this extension yields provably valid confidence sets in parameter inference problems in which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.

翻译：高保真模拟器作为连接理论模型与观测数据的桥梁，在众多科学领域中不可或缺。当模拟器与机器学习相结合时，无需显式使用似然函数，即可直接从真实观测与模拟观测中推断理论模型参数——这对似然函数难以解析计算的情况尤为重要。本文提出了一种对近期提出的无似然频率学派推断（LF2I）方法的简单扩展，该扩展具有计算优势。与LF2I类似，此扩展可在具备高保真模拟器的参数推断问题中生成可证明有效的置信集。我们通过三个具有教学意义的实例验证了该算法的实用性：前两个案例分别来自宇宙学及高能物理与天文学（二者均采用易处理似然函数），第三个来自流行病学（采用难处理似然函数）。