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\noindent Statistica Sinica {\bf 13}(2003), 443-460
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\centerline{\large\bf RANDOMIZED POLYA TREE MODELS FOR}
\vspace{2pt}
\centerline{\large\bf NONPARAMETRIC BAYESIAN INFERENCE}
\vspace{.25cm}
\centerline{Susan M. Paddock, Fabrizio Ruggeri, Michael Lavine and  Mike West}
\vspace{.4cm}
\centerline{\it RAND, CNR-IATMI, Duke University and Duke University}
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\begin{quotation}
\noindent {\it Abstract:}
Like other partition-based
models, Polya trees suffer the problem of partition dependence.  We
develop Randomized Polya Trees to address this limitation. This new
framework inherits the structure of Polya trees but ``jitters''
partition points and as a result smooths discontinuities in predictive
distributions. Some of the theoretical aspects of the new framework are
developed, followed by discussion of methodological and computational
issues arising in implementation. Examples of data analyses and prediction
problems are provided to highlight issues of Bayesian inference in this
context.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Bayesian nonparametrics, Bayesian
trees, partitioning, Polya tree prior, Randomized Polya tree.\par %r
\end{quotation}
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