The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of
parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et …

An important challenge in statistical modeling is to balance how well our model explains the
phenomenon under investigation with the parsimony of this explanation. In structural …

Discovering causal relationship using multivariate functional data has received a significant
amount of attention very recently. In this article, we introduce a functional linear structural …

Bayesian mixture models are widely used for clustering of high-dimensional data with
appropriate uncertainty quantification. However, as the dimension of the observations …

There has been increased research interest in the subfield of sparse Bayesian factor
analysis with shrinkage priors, which achieve additional sparsity beyond the natural …

This article is motivated by the problem of inference on interactions among chemical
exposures impacting human health outcomes. Chemicals often co-occur in the environment …

Infinite Mixtures of Infinite Factor Analysers Page 1 Bayesian Analysis (2020) 15, Number 3,
pp. 937–963 Infinite Mixtures of Infinite Factor Analysers Keefe Murphy ∗ , Cinzia Viroli † …

In many areas of science and engineering, computer simulations are widely used as proxies
for physical experiments, which can be infeasible or unethical. Such simulations are often …

High-dimensional categorical data are routinely collected in biomedical and social sciences.
It is of great importance to build interpretable parsimonious models that perform dimension …

Factor analysis provides a canonical framework for imposing lower-dimensional structure
such as sparse covariance in high-dimensional data. High-dimensional data on the same …