In this thesis, we develop nonparametric Bayesian models for learning ancestral genetic processes, which provide more flexible control over the complexity of the genetic data, and at the same time, utilize the structured data in a more principled way. Under a unified inheritance framework built on the assumption of hypothetical founder haplotypes that generate modern individual chromosomes, hierarchical Bayesian models based on Dirichlet process are developed for the following related applications in population genetics: the problem of haplotype inference from multi-population genotype data, joint inference of population structure and the recombination events, and the local ancestry estimation in admixed populations. This new approach allows one to explicitly exploit the shared structural information in the data from multiple populations. The resulting methods have shown to significantly outperform other existing methods that do not utilize such relatedness properly.
