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

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...

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Библиографические подробности
Опубликовано в: :Adv Neural Inf Process Syst
Главные авторы: Chan, Jeffrey, Perrone, Valerio, Spence, Jeffrey P., Jenkins, Paul A., Mathieson, Sara, Song, Yun S.
Формат: Artigo
Язык:Inglês
Опубликовано: 2018
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Online-ссылка:https://ncbi.nlm.nih.gov/pmc/articles/PMC7687905/
https://ncbi.nlm.nih.gov/pubmed/33244210
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