Spatial statistical models are commonly used in geographical scenarios to ensure spatial
variation is captured effectively. However, spatial models and cluster algorithms can be …

High-throughput sequencing technology has enabled researchers to profile microbial
communities from a variety of environments, but analysis of multivariate taxon count data …

This article focuses on model-based clustering of subjects based on the shared
relationships of subject-specific networks and covariates in scenarios when there are …

Many dose–response studies collect data on correlated outcomes. For example, in
developmental toxicity studies, uterine weight and presence of malformed pups are …

The supplementary material contains additional details on model specifications, as well as
derivations of the full conditional distributions needed for the Gibbs sampler for posterior …

We consider problems involving functional data where we have a collection of functions,
each viewed as a process realization, eg, a random curve or surface. For a particular …

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

We introduce a Bayesian spatial–temporal hierarchical multivariate probit regression model
that identifies weeks during the first trimester of pregnancy, which are impactful in terms of …

In this paper we present an infinite mixture model based on inverted Dirichlet distributions.
The proposed mixture is learned using a fully Bayesian approach and allows to overcome a …

We consider the setting of multivariate functional data collected over time at each of a set of
sites. Our objective is to implement model-based clustering of the functions across the sites …