{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-likelihood-free-inference-framework-for","title":"A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks","arxiv_id":"1802.06153","date":"2018-02-16","proceeding":"NeurIPS 2018 12","authors":["Jeffrey Chan","Valerio Perrone","Jeffrey P. Spence","Paul A. Jenkins","Sara Mathieson","Yun S. Song"],"abstract":"An explosion of high-throughput DNA sequencing in the past decade has led to\na surge of interest in population-scale inference with whole-genome data.\nRecent work in population genetics has centered on designing inference methods\nfor relatively simple model classes, and few scalable general-purpose inference\ntechniques exist for more realistic, complex models. To achieve this, two\ninferential challenges need to be addressed: (1) population data are\nexchangeable, calling for methods that efficiently exploit the symmetries of\nthe data, and (2) computing likelihoods is intractable as it requires\nintegrating over a set of correlated, extremely high-dimensional latent\nvariables. These challenges are traditionally tackled by likelihood-free\nmethods that use scientific simulators to generate datasets and reduce them to\nhand-designed, permutation-invariant summary statistics, often leading to\ninaccurate inference. In this work, we develop an exchangeable neural network\nthat performs summary statistic-free, likelihood-free inference. Our framework\ncan be applied in a black-box fashion across a variety of simulation-based\ntasks, both within and outside biology. We demonstrate the power of our\napproach on the recombination hotspot testing problem, outperforming the\nstate-of-the-art.","url_abs":"http://arxiv.org/abs/1802.06153v2","url_pdf":"http://arxiv.org/pdf/1802.06153v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-likelihood-free-inference-framework-for","repo_url":"https://github.com/popgenmethods/defiNETti","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.06153","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}