High-dimensional data sets have become ubiquitous in the past few decades, often with
many more covariates than observations. In the frequentist setting, penalized likelihood …

Gaussian graphical models provide a powerful framework to reveal the conditional
dependency structure between multivariate variables. The process of uncovering the …

We propose a Bayesian procedure for simultaneous variable and covariance selection
using continuous spike-and-slab priors in multivariate linear regression models where q …

We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable
selection in linear regression with grouped variables. We further extend the SSGL to sparse …

High-dimensional data, where the number of features or covariates can even be larger than
the number of independent samples, are ubiquitous and are encountered on a regular basis …

The auditory system comprises multiple subcortical brain structures that process and refine
incoming acoustic signals along the primary auditory pathway. Due to technical limitations of …

The soft computing technique, such as an artificial neural network (ANN) is a modelling tool
that is used to predict the pavement condition index value. ANN is used to model highly …

Gaussian graphical models can capture complex dependency structures among variables.
For such models, Bayesian inference is attractive as it provides principled ways to …

Consistent Group Selection with Bayesian High Dimensional Modeling Page 1 Bayesian
Analysis (2020) 15, Number 3, pp. 909–935 Consistent Group Selection with Bayesian High …

In this paper, we propose a novel Bayesian group regularization method based on the spike
and slab Lasso priors for jointly estimating multiple graphical models. The proposed method …