This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines these estimates using Markov Chain Monte Carlo procedures. This process is described in terms of NetPASS, a complex simulation based assessment in the domain of computer networking. The paper describes a parameterization of the relationships in NetPASS via an ordered polytomous item response model and details the updating of the model with observed data via Bayesian statistical procedures ultimately being provided by Markov Chain Monte Carlo estimation. (Contains 12 tables, 9 figures, and 47 references.) (Author/SLD)