EWF : simulating exact paths of the Wright--Fisher diffusion

1 code implementation • 13 Jan 2023 • Jaromir Sant, Paul A. Jenkins, Jere Koskela, Dario Spanò

The Wright--Fisher diffusion is important in population genetics in modelling the evolution of allele frequencies over time subject to the influence of biological phenomena such as selection, mutation, and genetic drift.

An estimator for the recombination rate from a continuously observed diffusion of haplotype frequencies

2 code implementations • 15 Dec 2022 • Robert C. Griffiths, Paul A. Jenkins

Estimators for the recombination rate, which are usually based on the idea of integrating over the unobserved possible evolutionary histories of a sample, can therefore be noisy.

Diffusion Limits at Small Times for Coalescent Processes with Mutation and Selection

no code implementations • 18 Dec 2020 • Philip A. Hanson, Paul A. Jenkins, Jere Koskela, Dario Spanò

The Ancestral Selection Graph (ASG) is an important genealogical process which extends the well-known Kingman coalescent to incorporate natural selection.

Probability Primary 60J90, 60F05, secondary 60J80

KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation

1 code implementation • 17 Dec 2020 • Anastasia Ignatieva, Rune B. Lyngsø, Paul A. Jenkins, Jotun Hein

The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population.

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

1 code implementation • NeurIPS 2018 • Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song

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.

Poisson Random Fields for Dynamic Feature Models

no code implementations • 22 Nov 2016 • Valerio Perrone, Paul A. Jenkins, Dario Spano, Yee Whye Teh

We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features.