Fire simulation codes are powerful tools for use in risk-informed and performance-based approaches for risk assessment. Given increasing use of fire simulation code results, accounting for the uncertainty inherent in fire simulation codes is becoming more important than ever. This research presents a "white-box" methodology with the goal of accounting for uncertainties resulting from simulation code. Uncertainties associated with the input variables used in the codes as well as the uncertainties associated with the sub-models and correlations used inside the simulation code are accounted for. A Bayesian estimation approach is used to integrate all evidence available and arrive at an estimate of the uncertainties associated with a parameter of interest being estimated by the simulation code. Two example applications of this methodology are presented.