IBM® SPSS® Amos™ 28
Bayesian estimation combines data together with any prior beliefs or knowledge about model parameters that the analyst may have, to arrive at a posterior distribution that summarizes the updated state of knowledge about the parameters. Bayesian estimation offers a number of benefits to structural equation modelers. Among them are
•Explicit incorporation of any available prior information or beliefs about model parameters
•Good performance in small samples
•Avoidance of inadmissible model parameter values (e.g., negative variances) through the choice of an appropriate prior distribution
•Estimation and hypothesis testing for any user-specified function of the model parameters
Examples 26 through 29 in the User's Guide demonstrate Bayesian estimation.
