\magnification=1200
\baselineskip=20pt
\nopagenumbers
\font\big=cmr12 scaled \magstep2

\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
\bigskip

\baselineskip=12pt
\centerline{4:15 p.m., Tuesday, April 18, 2000}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

\bigskip
\baselineskip=15pt
\centerline{\sl Michael A. Newton}
\centerline{\sl University of Wisconsin}
\bigskip
\centerline{\bf A nonparametric Bayes approach to infer the mixing distribution}
\bigskip


Routinely in statistical applications, hierarchical models arise in which
unobserved random effects contribute to heterogeneity amongst sampling
units.  An easily computable, smooth nonparametric estimate of the
underlying mixing distribution can be derived as an approximate
nonparametric Bayes estimate under a Dirichlet process prior.  I will
discuss the recursive estimation algorithm, its
consistency properties, and its application in several examples, including
its use as a model diagnostic in the analysis of microarray gene expression
data.

\bye