Thomas S. Ferguson works on statistics, probability theory and game theory. His UCLA page documents research on statistical decisions, stopping problems and games.[ref]
https://www.math.ucla.edu/~tom/. Author homepage and publications
His 1973 paper introduced a Bayesian framework built on the Dirichlet process, a probability distribution over probability measures. If the prior parameter measure is , observing changes it to . 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]
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Thomas S. Ferguson is a prominent figure in the field of statistics and decision theory, particularly known for his work in Bayesian statistics. He has made significant contributions to mathematical statistics, including the development of the Ferguson-Dirichlet process, which is a foundational concept in nonparametric Bayesian statistics. Ferguson's research includes topics such as estimation, Bayesian inference, and stochastic processes. His work has been influential in various applications, including genetics, economics, and machine learning.