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

ABSTRACT: An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate data

SUBMITTER: Chan J 

PROVIDER: S-EPMC7687905 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature