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. We introduce a simple modification of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. 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，在众多科学领域中是不可或缺的工具。当与机器学习结合时，模拟器能够直接从真实观测和模拟数据中推断理论模型参数，而无需显式使用似然函数。这一特性在似然函数不可处理时尤为重要。我们提出了一种对近期提出的无似然频率推断（LF2I）方法的简单改进，该方法具有计算优势。通过将我们的算法应用于三个具有教学意义的实例来展示其实用性：第一个实例来自宇宙学，第二个来自高能物理和天文学——两者均具有可处理的似然函数，而第三个实例来自流行病学，其似然函数不可处理。