Diagnostic assessments measure the knowledge, skills, and understandings of students at a smaller and more actionable grain size than traditional scale-score assessments. Results of diagnostic assessments are reported as a mastery profile, indicating which knowledge, skills, and understandings the student has mastered and which ones may need more instruction. These mastery decisions are based on probabilities of mastery derived from diagnostic classification models (DCMs). This report outlines a Bayesian framework for the estimation and evaluation of DCMs. Findings illustrate the utility of the Bayesian framework for estimating and evaluating DCMs in applied settings. Specifically, the findings demonstrate how a variety of DCMs can be defined within the same conceptual framework. Additionally, using this framework, the evaluation of model fit is more straightforward and easier to interpret with intuitive graphics. Throughout, recommendations are made for specific implementation decisions for the estimation process and the assessment of model fit.