Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data
Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address …

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 …

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 …

In linear mixed models, the assumption of normally distributed random effects is often
inappropriate and unnecessarily restrictive. The proposed approximate Dirichlet process …

We study the identification and consistency of Bayesian semiparametric IRT-type models,
where the uncertainty on the abilities' distribution is modeled using a prior distribution on the …

A novel semiparametric regression model is developed for evaluating the covariate‐specific
accuracy of a continuous medical test or biomarker. Ideally, studies designed to estimate or …

Adaptive Shrinkage in Pólya Tree Type Models Page 1 Bayesian Analysis (2017) 12,
Number 3, pp. 779–805 Adaptive Shrinkage in Pólya Tree Type Models Li Ma ∗,† Abstract …

Incorporating temporal and spatial variation could potentially enhance information gathered
from survival data. This paper proposes a Bayesian semi-parametric model for capturing …

Polya trees (PT) are random probability measures which can assign probability 1 to the set
of continuous distributions for certain specifications of the hyperparameters. This feature …

We present a simple, efficient, and computationally cheap sampling method for exploring an
unnormalized multivariate density on ℝ d, such as a posterior density, called the Polya tree …