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：似然剖面是一种用于参数估计、不确定性量化和实际可识别性分析的高效且强大的频率学派方法。遗憾的是，这些方法无法轻易应用于那些没有易处理似然函数的随机模型。这类模型在许多科学领域中很常见，导致经典方法在这些场景中难以应用。为解决这一局限，我们开发了一种新方法，将似然剖面方法推广到似然无法计算但能进行假设数据生成过程的随机模拟的情形。我们的方法基于将广义贝叶斯推断的进展重新诠释到频率学派框架中。我们推导了一种构建广义似然剖面的方法，并通过校准这些剖面以实现给定置信水平下所需的频率学派覆盖。我们通过文献中的实际案例展示了该方法的性能，并强调了其在处理难以计算似然模型的实际可识别性分析中的能力。