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

NeurIPS 2018 Jeffrey ChanValerio PerroneJeffrey P. SpencePaul A. JenkinsSara MathiesonYun S. Song

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... (read more)

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