Abstract
Nonparametric Bayesian inference is a research topic in the fields of computational statistics and machine learning. The review conducts a comprehensive survey on one of the representative stochastic processes-Dirichlet process (DP), including theoretical fundamentals, representation & construction method, extensions, statistical inference and applications in machine learning & bioinformatics. By analyzing the relations between Dirichlet process and hierarchical Dirichlet process, nested Dirichlet process, dependent Dirichlet process, matrix stick-breaking process and kernel stick-breaking process, the review reveals how to extend Dirichlet process to specific application scenario. To conclude, the review analyses DP merits and demerits and discusses DP future research focus.
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