Jayaram Sethuraman


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  • Speaker: Jayaram Sethuraman
  • Host Department: Statistics
  • Date: 10/04/2013
  • Time: 11:30 AM

  • Location: 411 West Hall

  • Description:

    Following standard parametric Bayes applications, there was a
    growing need for nonparametric Bayes analysis which seemed to
    pose some difficulties. These difficulties were removed by the
    the first definitions of Dirichlet processes by Ferguson,
    Blackwell and MacQueen. Dirichlet processes became famous after
    some easy applications in standard nonparametric Bayes
    problems. However, the proofs looked complicated and
    mystifying. In spite of this many salient properties of Dirichlet
    processes were being established.

    The Sethuraman constructive definition of a Dirichlet process
    simplified proving such properties and establishing further
    properties. Computational Bayes analysis took off in parametric
    problems, thanks to MCMC. The importance of the constructive
    definition for computational nonparametric Bayes analysis was
    recognized immediately and it opened the flood gates for a
    multitude of diverse applications.

    This talk will traverse through some of these developments.