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Found 3 papers, 3 papers with code
grenedalf: population genetic statistics for the next generation of pool sequencing
Pool sequencing is an efficient method for capturing genome-wide allele frequencies from multiple individuals, with broad applications such as studying adaptation in Evolve-and-Resequence experiments, monitoring of genetic diversity in wild populations, and genotype-to-phenotype mapping.
Flexible mean field variational inference using mixtures of non-overlapping exponential families
When all distributions in the model are members of exponential families and are conditionally conjugate, optimization schemes can often be derived by hand.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
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.
