Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific
computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is …

There is a growing interest in learning how the distribution of a response variable changes
with a set of observed predictors. Bayesian nonparametric dependent mixture models …

We establish conditions under which Metropolis-Hastings (MH) algorithms with a position-
dependent proposal covariance matrix will or will not have the geometric rate of …

Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings
samplers, are commonly used for sampling from multivariate probability distributions. A long …

We establish asymptotic normality of the batch means estimator of MCMC variance for
reversible geometrically ergodic chains. Existing results use assumptions which are not …

A double Pólya-Gamma data augmentation scheme is developed for posterior sampling
from a Bayesian hierarchical model of total and categorical count data. The scheme applies …

Generalized linear mixed models (GLMMs) are often used for analyzing correlated non-
Gaussian data. The likelihood function in a GLMM is available only as an intractable high …

Use of continuous shrinkage priors—with a “spike” near zero and heavy-tails towards infinity—
is an increasingly popular approach to induce sparsity in parameter estimates. When the …

Finite sample inference for Cox models is an important problem in many settings, such as
clinical trials. Bayesian procedures provide a means for finite sample inference and …

Data augmentation (DA) algorithms are slow in massive data settings due to multiple passes
through the entire data. We address this problem by developing a DA extension that exploits …