Dynamic risk analysis (DRA) has been used widely to analyze the performance of alarm and safety interlock systems of manufacturing processes. Because the most critical alarm and safety interlock systems are rarely activated, little or no data from these systems are often available to apply purely-statistical DRA methods. Moskowitz et al. (2015) 1 introduced a repeated-simulation, process-model-based technique for constructing informed prior distributions, generating low-variance posterior distributions for Bayesian analysis, 1 and making alarm-performance predictions. This article presents a method of quantifying process model quality, which impacts prior and posterior distributions used in Bayesian Analysis. The method uses higher-frequency alarm and process data to select the most relevant constitutive equations and assumptions. New data-based probabilistic models that describe important special-cause event occurrences and operators' response-times are proposed and validated with industrial plant data. These models can be used to improve estimates of failure probabilities for alarm and safety interlock systems. VC 2016 American Institute of Chemical Engineers