Bayesian inference is a principled framework for dealing with uncertainty. The practitioner can perform an initial assumption for the physical phenomenon they want to model (prior belief), collect some data and then adjust the initial assumption in the light of the new evidence (posterior belief). Approximate Bayesian Computation (ABC) methods, also known as likelihood-free inference techniques, are a class of models used for performing inference when the likelihood is intractable. The unique requirement of these models is a black-box sampling machine. Due to the modelling-freedom they provide these approaches are particularly captivating. Robust Optimisation Monte Carlo (ROMC) is one of the most recent techniques of the specific domain. It approximates the posterior distribution by solving independent optimisation problems. This dissertation focuses on the implementation of the ROMC method in the software package Engine for Likelihood-Free Inference (ELFI). In the first chapters, we provide the mathematical formulation and the algorithmic description of the ROMC approach. In the following chapters, we describe our implementation; (a) we present all the functionalities provided to the user and (b) we demonstrate how to perform inference on some real examples. Our implementation provides a robust and efficient solution to a practitioner who wants to perform inference on a simulator-based model. Furthermore, it exploits parallel processing for accelerating the inference wherever it is possible. Finally, it has been designed to serve extensibility; the user can easily replace specific subparts of the method without significant overhead on the development side. Therefore, it can be used by a researcher for further experimentation.

中文翻译：

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扩展统计软件包 Engine for Likelihood-Free Inference

贝叶斯推理是处理不确定性的原则框架。从业者可以对他们想要建模的物理现象进行初始假设（先验信念），收集一些数据，然后根据新证据调整初始假设（后验信念）。近似贝叶斯计算 (ABC) 方法，也称为无似然推理技术，是一类用于在似然难以处理时执行推理的模型。这些型号的独特要求是黑盒取样机。由于建模自由，他们提供的这些方法特别吸引人。鲁棒优化蒙特卡罗 (ROMC) 是特定领域的最新技术之一。它通过解决独立的优化问题来近似后验分布。本论文重点研究了ROMC方法在ELFI软件包Engine中的实现。在第一章中，我们提供了 ROMC 方法的数学公式和算法描述。在接下来的章节中，我们将描述我们的实现；(a) 我们展示了提供给用户的所有功能，以及 (b) 我们演示了如何对一些真实的例子进行推理。我们的实现为想要在基于模拟器的模型上执行推理的从业者提供了强大而有效的解决方案。此外，它利用并行处理在可能的情况下加速推理。最后，它旨在为可扩展性服务；用户可以轻松替换该方法的特定子部分，而无需在开发方面产生大量开销。所以，