This paper describes a Bayesian network model for a candidate assessment design that had four proficiency variables and 48 tasks with 3-12 observable outcome variables per task and scale anchors to identify the location of the subscales. The domain experts’ view of the relationship among proficiencies and tasks established a complex prior distribution over 585 parameters. Markov Chain Monte Carlo (MCMC) estimation recovered the parameters of data simulated from the expert model. The sample size and the strength of the prior had only a modest effect on parameter recovery, but did affect the standard error of estimated parameters. Finally, an identifiability issue involving relabeling of proficiency states and permutations of the matrixes is addressed in the context of this study.