Recent literature has introduced (a) the network perspective to psychology and (b) collection
of time series data to capture symptom fluctuations and other time varying factors in daily life …

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

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

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and
applied to multi-subject functional connectivity estimation in the human brain. It enables …

Bayesian structure learning in Gaussian graphical models is often done by search
algorithms over the graph space. The conjugate prior for the precision matrix satisfying …

To understand psychological data, it is crucial to examine the structure and dimensions of
variables. In this study, we examined alternative estimation algorithms to the conventional …

Abstract Markov chain Monte Carlo (MCMC) is a powerful methodology for the
approximation of posterior distributions. However, the iterative nature of MCMC does not …

The prevalence of chronic non‐communicable diseases such as obesity has noticeably
increased in the last decade. The study of these diseases in early life is of paramount …

Gaussian graphical models depict the conditional dependencies between variables within a
multivariate normal distribution in a graphical format. The identification of these graph …

Decision trees are flexible models that are well suited for many statistical regression
problems. In the Bayesian framework for regression trees, Markov Chain Monte Carlo …