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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 p.m., Tuesday, October 16, 2007\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Yuguo Chen} \\
  Department of Statistics\\
  University of Illinois at Urbana-Champaign
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  \subsection*{A New Data Augmentation Scheme}
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\noindent

Motivated by the statistical inference problem in population genetics, we
present a new data augmentation scheme which leads to a more efficient
Markov chain Monte Carlo (MCMC) algorithm. We show that the new algorithm
converges to the target distribution faster than the standard MCMC
algorithm. We apply our method to examples from population genetics to
demonstrate its efficiency.


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