Causal discovery, the inference of causal relations among variables from data, is a
fundamental problem of science. Nowadays, due to an increased awareness of data privacy …

A brain-computer interface (BCI) is a system that translates brain activity into commands to
operate technology. A common design for an electroencephalogram (EEG) BCI relies on the …

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For
spatially dependent signals in high-dimensional and large-scale data, such as …

We introduce Bayesian Gaussian graphical models with covariates (GGMx), a class of
multivariate Gaussian distributions with covariate-dependent sparse precision matrix. We …

Causal mediation analysis aims to characterize an exposure's effect on an outcome and
quantify the indirect effect that acts through a given mediator or a group of mediators of …

We consider the task of learning causal structures from data stored on multiple machines,
and propose a novel structure learning method called distributed annealing on regularized …

We consider Bayesian high‐dimensional mediation analysis to identify among a large set of
correlated potential mediators the active ones that mediate the effect from an exposure …

Motivated by the analysis of spectrometric data, a Gaussian graphical model for learning the
dependence structure among frequency bands of the infrared absorbance spectrum is …

Increasing evidence has shown that gene‐gene interactions have important effects in
biological processes of human diseases. Due to the high dimensionality of genetic …

Time series data arising in many applications nowadays are high-dimensional. A large
number of parameters describe features of these time series. We propose a novel approach …