The foundation of the subject of nonparametric Bayesian inference was laid in two technical
reports: a 1969 UCLA report by Thomas S. Ferguson (later published in 1973 as a paper in …

Ferguson and Phadia have recently discussed the nonparametric Bayesian estimation of a
distribution function from a right-censored random sample using process priors that are …

Problems of statistical inference with an infinite dimensional parameter space, usually a
space of probability distributions over a set, are of great importance both theoretically and …

The Bayesian approach to statistical problems, though fruitful in many ways, has been rather
unsuccessful in treating nonparametric problems. This is due primarily to the difficulty in …

Bayesian nonparametric inference is a relatively young area of research and it has recently
undergone a strong development. Most of its success can be explained by the considerable …

It is now possible to demonstrate many applications of Bayesian nonparametric methods. It
works. Itisclear, however, thatnonparametric methods are more complicatedtounderstand …

This chapter provides a brief review of some large sample frequentist properties of
nonpal'ametric Bayesian procedures. The review is not comprehensive, but rather, is meant …

Implementation of a full Bayesian non‐parametric analysis involving neutral to the right
processes (apart from the special case of the Dirichlet process) has been difficult for two …

A family of random probabilities is defined and studied. This family contains the Dirichlet
process as a special case, corresponding to an inner point in the appropriate parameter …

Let X1,⋯, Xn be a random sample from an unknown, let y1,⋯, yn be known real constants,
and let Zi= min (Xi, yi), i= 1,⋯, n. It is required to estimate F on the basis of the observations …