 It is often used in Bayesian inference to describe the prior knowledge about the distribution of random variables—how likely it is that the random variables are distributed according to one or another particular distribution.The Dirichlet process is specified by a base distribution formula_1 and a positive real number formula_2 called the concentration parameter (also known as scaling parameter).The base distribution is the expected value of the process, i.e., the Dirichlet process draws distributions "around" the base distribution the way a normal distribution draws real numbers around its mean.