probabilistic graphical modeling
Definition
Probabilistic graphical modeling refers to a statistical framework in which nodes represent variables and edges represent statistical relationships between them, enabling the visualization and estimation of conditional dependencies in multivariate data. Bayesian Gaussian Graphical Models (BGGMs) are a specific class of probabilistic graphical models used in network psychometrics that estimate regularized partial correlations—adjusted relationships between variables that account for the influence of other variables in the network. A generalized approach to BGGMs employs Cholesky decomposition of correlation matrices to enable flexible estimation across diverse data types (binary, ordinal, continuous) while allowing researchers to specify various priors and likelihoods for model parameters.
Sources: Franco et al. (2024)
Related Terms
Applications
Probabilistic Graphical Modeling and Network Psychometrics
Probabilistic graphical models employ a generalized approach in network psychometrics, with applications across diverse psychological fields. Contemporary network psychometrics employs graphical models where nodes represent observed variables and edges represent statistical relationships between them.
Sources: Franco et al. (2024)
Probabilistic Graphical Modeling and Partial Correlations
In probabilistic graphical models such as Gaussian Graphical Models, each edge represents the partial correlation between nodes, reflecting conditional dependence given the remaining variables in the network. Partial correlations are adjusted for the influence of other variables.
Sources: Franco et al. (2024)
