A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that
parameters are endowed with distributions which may themselves introduce new …

Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …

Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

We present the nested Chinese restaurant process (nCRP), a stochastic process that
assigns probability distributions to ensembles of infinitely deep, infinitely branching trees …

Bayesian Cluster Analysis: Point Estimation and Credible Balls (with Discussion) Page 1
Bayesian Analysis (2018) 13, Number 2, pp. 559–626 Bayesian Cluster Analysis: Point …

We develop the distance dependent Chinese restaurant process, a flexible class of
distributions over partitions that allows for dependencies between the elements. This class …

In multicenter studies, subjects in different centers may have different outcome distributions.
This article is motivated by the problem of nonparametric modeling of these distributions …

We propose a class of kernel stick-breaking processes for uncountable collections of
dependent random probability measures. The process is constructed by first introducing an …

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain
modeling flexibility and robustness against mis-specification of the probability model. In the …