Efficient inference methods for large-scale population genomics
Yun Song
University of California, Berkeley (UC Berkeley)
Many applications in population genetics utilize sampling distributions, which describe the probability of observing a sample of DNA sequences randomly drawn from a population. Unfortunately, exact sampling distributions are unknown for most models of evolution, particularly when natural selection and recombination are taken into account. In this talk, I will summarize our recent progress on this challenging problem and describe how mathematically sound approximations can be made to obtain scalable inference methods for population genomic analyses.