Introducing of Dirichlet process prior in the Nonparametric Bayesian models frame work

Statistical models are utilized to learn about the mechanism that the data are generating from it. Often it is assumed that the random variables y_i،i=1،…،n، are samples from the probability distribution F which is belong to a parametric distributions class. However، in practice، a parametric model may be inappropriate to describe the data. In this settings، the parametric assumption could be relaxed and more flexible models could be used analysis of data. In the nonparametric Bayes approach، a prior distributions is defined over the whole space of probability distributions for random variable distribution. Due to the Dirichlet process (DP) has interesting properties، it is thus used extensively. In this paper، we introduce DP and its features.