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By Thomas S. Ferguson. The Annals of Statistics 1(2), 209–230.

Original paper: users.wpi.edu/~balnan/Ferguson1973.pdf

Sections 3–4 define the Dirichlet process and its discrete realization; Section 3, Theorem 1, gives the posterior update by adding point masses at observations. The wiki uses concentration plus a normalized base measure instead of the paper's single finite nonzero parameter measure.

Thomas S. Ferguson works on statistics, probability theory and game theory. His UCLA page documents research on statistical decisions, stopping problems and games.^[ref]

His 1973 paper introduced a Bayesian framework built on the Dirichlet process, a probability distribution over probability measures. If the prior parameter measure is $\alpha$, observing $x_1,\dots ,x_n$ changes it to $\alpha +{\sum }_i{\delta }_{x_i}$. This conjugate update extends the familiar idea of adding category counts to inference about an entire unknown distribution, providing a foundation for Bayesian nonparametric statistics.^[ref]