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A likelihood-free inference framework for population genetic data using exchangeable neural networks
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Chan, J., Perrone, Valerio, Spence, J. P., Jenkins, Paul, Mathieson, S. and Song, Y. S. (2019) A likelihood-free inference framework for population genetic data using exchangeable neural networks. In: Bengio, Samy and Wallach, Hanna M. and Larochelle, Hugo and Grauman, Kristen and Cesa-Bianchi, Nicolò, (eds.) Advances in Neural Information Processing Systems 31 (NIPS 2018). Curran Associates, Inc, pp. 8594-8605.
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Official URL: https://doi.org/10.1101/267211
Abstract
Inference for population genetics models is hindered by computationally intractable likelihoods. While this issue is tackled by likelihood-free methods, these approaches typically rely on hand-crafted summary statistics of the data. In complex settings, designing and selecting suitable summary statistics is problematic and results are very sensitive to such choices. In this paper, we learn the first exchangeable feature representation for population genetic data to work directly with genotype data. This is achieved by means of a novel Bayesian likelihood-free inference framework, where a permutation-invariant convolutional neural network learns the inverse functional relationship from the data to the posterior. We leverage access to scientific simulators to learn such likelihood-free function mappings, and establish a general framework for inference in a variety of simulation-based tasks. We demonstrate the power of our method on the recombination hotspot testing problem, outperforming the state-of-the-art.
Item Type: | Book Item | ||||||||||||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||||||||
Journal or Publication Title: | Advances in Neural Information Processing Systems 31 (NIPS 2018) | ||||||||||||||||||
Publisher: | Curran Associates, Inc | ||||||||||||||||||
Book Title: | Advances in Neural Information Processing Systems 31 (NIPS 2018) | ||||||||||||||||||
Editor: | Bengio, Samy and Wallach, Hanna M. and Larochelle, Hugo and Grauman, Kristen and Cesa-Bianchi, Nicolò | ||||||||||||||||||
Official Date: | 2019 | ||||||||||||||||||
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Page Range: | pp. 8594-8605 | ||||||||||||||||||
DOI: | 10.1101/267211 | ||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
Date of first compliant deposit: | 29 October 2018 | ||||||||||||||||||
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Conference Paper Type: | Paper | ||||||||||||||||||
Title of Event: | 32nd Conference on Neural Information Processing Systems (NIPS 2018) | ||||||||||||||||||
Type of Event: | Conference | ||||||||||||||||||
Location of Event: | Montréal, Canada | ||||||||||||||||||
Date(s) of Event: | 2-8 Dec 2018 | ||||||||||||||||||
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