Structural Equation Models with latent variables (SEM) are hypothetical constructs used to represent causality relationships in data, where the observed correlation structure is transferred into the correlation structure of latent variables. In this paper a Bayesian analysis of SEM is proposed using parameter expansion to overcome identi fiability issues. An original use of posterior draws from latent variables is proposed to model expert knowledge in uncertainty analysis.