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Dirichlet process

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In probability theory, Dirichlet processes (after the distribution associated with Peter Gustav Lejeune Dirichlet) are a family of stochastic processes whose realizations are probability distributions. In other words, a Dirichlet process is a probability distribution whose range is itself a set of probability distributions. 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.

Dirichlet process - Wikipedia Jump to content From Wikipedia, the free encyclopedia Family of stochastic processes Draws from the Dirichlet process DP ⁡ ( N ( 0 , 1 ) , α ) {\displaystyle \operatorname {DP} (N(0,1),\alpha )} . The four rows use different alpha α {\displaystyle \alpha } (top to bottom: 1, 10, 100 and 1000) and each row contains three repetitions of the same experiment. As seen from the graphs, draws from a Dirichlet process are discrete distributions and they become less concentrated (more spread out) with increasing α {\displaystyle \alpha } . The g

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