The document discusses representing belief uncertainty in software models. It proposes using a Bayesian probability approach to quantify belief uncertainty, where degrees of belief for model statements are assigned by belief agents. A UML profile and operational semantics are defined to explicitly represent belief agents and propagate credence values through dependent statements. This allows querying credence values to understand the level of confidence in different parts of the model based on the assessing agent. Future work includes associating evidence with beliefs and applying this approach to other model types.