Easy High-Dimensional Likelihood-Free Inference

TL;DR

This paper presents a novel likelihood-free inference framework using GANs that replaces traditional simulators with neural networks, enabling scalable inference for high-dimensional and complex data distributions.

Contribution

It introduces a data-driven approach that improves scalability and handles high-dimensional data better than existing methods.

Findings

Outperforms existing methods on benchmark datasets

Handles high-dimensional and complex distributions effectively

Replaces black-box simulators with neural network approximators

Abstract

We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion. On benchmark data sets, our approach improves on others with respect to scalability, ability to handle high dimensional data and complex probability distributions.