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An explosion of high-throughput DNA sequencing in the past decade has led toa surge of interest in population-scale inference with whole-genome data.Recent work in population genetics has centered on designing inference methodsfor relatively simple model classes, and few scalable general-purpose inferencetechniques exist for more realistic, complex models. To achieve this, twoinferential challenges need to be addressed: (1) population data areexchangeable, calling for methods that efficiently exploit the symmetries ofthe data, and (2) computing likelihoods is intractable as it requiresintegrating over a set of correlated, extremely high-dimensional latentvariables. These challenges are traditionally tackled by likelihood-freemethods that use scientific simulators to generate datasets and reduce them tohand-designed, permutation-invariant summary statistics, often leading toinaccurate inference. In this work, we develop an exchangeable neural networkthat performs summary statistic-free, likelihood-free inference. Our frameworkcan be applied in a black-box fashion across a variety of simulation-basedtasks, both within and outside biology. We demonstrate the power of ourapproach on the recombination hotspot testing problem, outperforming thestate-of-the-art.

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks