Extending the statistical software package Engine for Likelihood-Free Inference

TL;DR

This paper details the implementation of the Robust Optimisation Monte Carlo (ROMC) method within the Engine for Likelihood-Free Inference (ELFI) software, enhancing likelihood-free Bayesian inference with a flexible, efficient, and extensible tool that leverages parallel processing.

Contribution

It introduces a new implementation of the ROMC method in ELFI, providing a robust, efficient, and extensible tool for likelihood-free Bayesian inference with practical demonstrations.

Findings

Implementation offers a user-friendly interface for ROMC in ELFI.

The software exploits parallel processing for faster inference.

Designed for extensibility to facilitate further research.

Abstract

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.