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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15, Tuesday, October 2, 2007\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Persi Diaconis} \\
  Statistics Department\\
  Stanford University
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  \subsection*{Conditional Inference:  Fisher, Neyman, and Bayes}
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\noindent

Conditional inference is one of the thorny problems of classical statistics.  It can also
lead to hard math problems, things like generating from logistic regression models given the
sufficient statistics lead to "algebraic statistics."  In this talk, I will briefly sketch how these
ideas can be extended to continuous variants (test if the data is from some gamma, scale and shape
unknown).  I will also revisit the Bayesian approach and show how the likelihood principle is
usually misused.  There is still lots to think about.  All of this is joint work with Susan Holmes.

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