Bayesian inference
Definition
Bayesian inference is a statistical framework that updates prior beliefs about parameter values and network structures into posterior distributions after observing data, enabling researchers to quantify uncertainty in estimates and compare competing hypotheses. In graphical modeling for psychology, Bayesian inference uses prior distributions with observed data to produce posterior distributions. Through Bayesian model-averaging and Bayes factors, researchers can determine posterior probabilities for edge inclusion and exclusion in network models and obtain credible intervals for partial association parameters. This approach offers advantages by explicitly quantifying uncertainty about both the network structure and parameter estimates.
Sources: Huth et al. (2024)
Related Terms
Applications
Bayesian Inference and Graphical Models
Bayesian inference is used to estimate the structure and parameters of graphical models, which represent psychological constructs as systems of interacting variables. By applying Bayesian methods to graphical models fitted to cross-sectional data, researchers can infer which edges are present or absent and quantify the uncertainty associated with these structural estimates.
Sources: Huth et al. (2024)
Bayesian Inference and Network Psychometrics
In network psychometrics, Bayesian inference enables researchers to model psychological constructs as phenomena emerging through systems of interacting variables. This approach allows researchers to draw inferences about conditional dependencies between psychological variables and to obtain evidence for edge inclusion or exclusion through Bayes factors.
Sources: Huth et al. (2024)
