  In recent years, surrogate models have been successfully used in
likelihood-free inference to decrease the number of simulator evaluations. The
current state-of-the-art performance for this task has been achieved by
Bayesian Optimization with Gaussian Processes (GPs). While this combination
works well for unimodal target distributions, it is restricting the flexibility
and applicability of Bayesian Optimization for accelerating likelihood-free
inference more generally. We address this problem by proposing a Deep Gaussian
Process (DGP) surrogate model that can handle more irregularly behaved target
distributions. Our experiments show how DGPs can outperform GPs on objective
functions with multimodal distributions and maintain a comparable performance
in unimodal cases. This confirms that DGPs as surrogate models can extend the
applicability of Bayesian Optimization for likelihood-free inference (BOLFI),
while adding computational overhead that remains negligible for computationally
intensive simulators.
