Data science can be defined as the interaction between computer programming, statistical
learning, and one of the many possible domains where it can be applied. In the paper we …

We specify some conditions for the identification of a multi‐factor model with correlated
residuals, uncorrelated factors and zero restrictions in the factor loadings. These conditions …

We introduce a Bayesian approach to model assessment in the class of graphical vector
autoregressive processes. As a result of the very large number of model structures that may …

Principal component analysis and factor analysis are fundamental multivariate analysis
methods. In this paper a unified framework to connect them is introduced. Under a general …

A fundamental goal of unsupervised feature selection is denoising, which aims to identify
and reduce noisy features that are not discriminative. Due to the lack of information about …

We present a methodology for Bayesian model choice and averaging in Gaussian directed
acyclic graphs (dags). The dimension-changing move involves adding or dropping a …

Based on a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm which was
developed by Fronk and Giudici (2000) to deal with model selection for Gaussian dags, we …

We consider the problem of estimating high-dimensional covariance matrices of a particular
structure, which is a summation of low rank and sparse matrices. This covariance structure …

Non-Gaussian factor analysis differs from ordinary factor analysis because of the distribution
assumption on the factors which are modelled by univariate mixtures of Gaussians thus …

We consider the problem of estimating covariance matrices of a particular structure that is a
summation of a low-rank component and a sparse component. This is a general covariance …