Abstract: Dirichlet processes are a type of stochastic processes widely used in nonparametric Bayesian models, especially in research that involves probabilistic graphical models. Over the past few years, significant effort has been made in the study of such processes, mainly due to their modeling flexibility and wide applicability. For instance, Dirichlet processes are capable of learning the number of clusters as well as the corresponding parameters of each cluster whereas other clustering or classification models usually are not able to. In this survey, we first introduce the definitions of Dirichlet processes. We then present Dirichlet process mixture models and their applications, and discuss in detail hierarchical Dirichlet processes (HDP), their roles in constructing other models, and examples of related applications in many important fields. Finally, we summarize recent developments in the study and applications of hierarchical Dirichlet processes and offer our remarks on future research.