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.