关键词：DNA测序；人口基因组；可伸缩算法
摘 要：In this thesis we present two novel algorithms that make use of DNA sequencing data in a principled yet practical way. The fi rst method estimates the history of eff ective population sizes of a species using a coalescent hidden Markov model (HMM). Previous coalescent HMMs could only handle a few sequences, since the set of coalescent trees makes the statespace prohibitively large. Our algorithm uses a modifi ed state-space to make inference computationally feasible while still retaining the essential genealogical features of a sample. We apply this algorithm, called diCal, to human data to learn more about major events in human history, such as the out-of-Africa migration. We also provide several extensions to diCal that make the computation faster, more automated, and applicable in a wider variety of scenarios.