Bayesian graphical modeling in network psychometrics

Network psychometrics uses undirected graphical models to model the network structure of complex psychosocial systems. This talk will introduce Bayesian graphical modeling, the Bayesian approach to the analysis of psychological networks and the topic of this symposium. Estimating the causal structure of a psychosocial system from correlational data is extremely difficult, so the field has focused instead on estimating the conditional independence and dependence structure. In this context, Markov Random Field (MRF) models are an important class of undirected graphical models because their parameters provide direct information about the conditional independence structure of the underlying system. We discuss new and old MRF models for psychological variables, especially binary and ordinal variables, and the computational and conceptual challenges in their Bayesian analysis. The ultimate goal of these analyses is the Bayes factor test of conditional independence, and we discuss how this significantly advances the field of network psychometrics.