Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models without a tractable likelihood function. Such models are typical in many fields of science, rendering these classical approaches impractical in these settings. To address this limitation, we develop a new approach to generalising the methods of likelihood profiling for situations when the likelihood cannot be evaluated but stochastic simulations of the assumed data generating process are possible. Our approach is based upon recasting developments from generalised Bayesian inference into a frequentist setting. We derive a method for constructing generalised likelihood profiles and calibrating these profiles to achieve desired frequentist coverage for a given coverage level. We demonstrate the performance of our method on realistic examples from the literature and highlight the capability of our approach for the purpose of practical identifability analysis for models with intractable likelihoods.

翻译：似然剖面是一种高效且强大的频率学派方法，用于参数估计、不确定性量化和实际可辨识性分析。然而，对于缺乏可处理似然函数的随机模型，这些方法难以直接应用。这类模型在许多科学领域中普遍存在，导致经典方法在这些场景中不适用。为解决这一局限性，我们开发了一种新方法，将似然剖面方法推广至似然无法计算但可对假设数据生成过程进行随机模拟的情形。我们的方法基于将广义贝叶斯推断的进展重新融入到频率学派框架中。我们提出了一种构建广义似然剖面的方法，并对其进行校准，以确保在给定置信水平下达到期望的频率学派覆盖率。我们在文献中的实际示例上验证了所提方法的性能，并强调了该方法在针对具有不可处理似然模型进行实际可辨识性分析方面的能力。