  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.
