Title
Approximate Bayesian Computation in Population Genetics
Speaker
Mark Beaumont, Animal and Microbial Sciences, University of Reading, UK
Abstract
Recently a group of techniques, variously called likelihood-free inference, or Approximate Bayesian Computation (ABC), have been quite widely applied in population genetics. These methods typically require the data to be compressed into summary statistics. A large number of simulations are then performed. The main idea is that an approximation of the likelihood—in this case the probability of obtaining the observed summary statistics measured from the data—is proportional to the number of simulated points that lie within some small distance of the observed point. With this approximation it is then possible to apply all standard likelihood-based techniques for inference, both frequentist and Bayesian. This talk gives examples of the application of these techniques to a variety of problems, and finishes by describing recent work in which the ABC method can be used to detect loci under local selection.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
