Bayesian inference
Definition
Bayesian inference is a statistical framework in which prior beliefs about model parameters and structures are updated with observed data through Bayes' rule, yielding a posterior distribution that encodes everything known about those quantities after observing the data. In graphical modeling for psychology, this approach allows researchers to quantify uncertainty in both network structure, specifically which edges are present or absent among interacting variables, and edge weight parameters. Posterior edge inclusion probabilities and inclusion Bayes factors are the primary inferential outputs, where a Bayes factor greater than 1 indicates support for conditional dependence between two nodes and a value less than 1 indicates support for conditional independence. The easybgm package was designed to make this framework accessible to applied researchers with little prior experience in Bayesian methods, consolidating estimation routines for binary, ordinal, and continuous cross-sectional data into a single workflow.
Sources: Huth et al. (2024)
Related Terms
- network psychometrics (1 shared article)
- tutorial (1 shared article)
- R (1 shared article)
Applications
Bayesian Inference and Graphical Models
Bayesian inference is applied to graphical models to estimate both the network structure and the partial association parameters that define edge weights among psychological variables. By treating each possible graph configuration as a separate statistical model and averaging across all of them through Bayesian model-averaging, researchers avoid committing to a single network structure and instead obtain posterior probabilities for each edge. Credible intervals and posterior standard deviations further characterize uncertainty in the strength of those associations.
Sources: Huth et al. (2024)
Bayesian Inference and Network Psychometrics
Network psychometrics models psychological constructs as systems of interacting variables, and Bayesian inference provides the means to quantify uncertainty in those systems more directly than frequentist alternatives. Researchers have applied Bayesian methods in this area specifically because they yield evidence for the inclusion or exclusion of individual edges, which is central to interpreting the conditional dependence structure of variables such as disorder symptoms or educational test scores.
Sources: Huth et al. (2024)
