Performing inference in statistical models with an intractable likelihood is
challenging, therefore, most likelihood-free inference (LFI) methods encounter
accuracy and efficiency limitations. In this paper, we present the
implementation of the LFI method Robust Optimisation Monte Carlo (ROMC) in the
Python package ELFI. ROMC is a novel and efficient (highly-parallelizable) LFI
framework that provides accurate weighted samples from the posterior. Our
implementation can be used in two ways. First, a scientist may use it as an
out-of-the-box LFI algorithm; we provide an easy-to-use API harmonized with the
principles of ELFI, enabling effortless comparisons with the rest of the
methods included in the package. Additionally, we have carefully split ROMC
into isolated components for supporting extensibility. A researcher may
experiment with novel method(s) for solving part(s) of ROMC without
reimplementing everything from scratch. In both scenarios, the ROMC parts can
run in a fully-parallelized manner, exploiting all CPU cores. We also provide
helpful functionalities for (i) inspecting the inference process and (ii)
evaluating the obtained samples. Finally, we test the robustness of our
implementation on some typical LFI examples.

利用 Python 软件包 ELFI 实现的、前瞻性的高度并行化的 Robust Optimisation Monte Carlo（ROMC）似然无关推断（LFI）框架，用于从后验中提供准确的加权样本，可被科学家用作即插即用的 LFI 算法，也支持独立扩展 ROMC 的组件以进行方法解决，具有完全并行运行的功能，并提供了有助于检查推断过程和评估获得样本的功能，最后在典型的 LFI 例子上测试了实现的鲁棒性。