This study develops probabilistic estimates of ozone (O3) sensitivities to precursor emissions by incorporating uncertainties in photochemical modeling and evaluating model performance based on ground-level observations of O3 and oxides of nitrogen (NOx). Uncertainties in model formulations and input parameters are jointly considered to identify factors that strongly influence O3 concentrations and sensitivities in the Dallas-Fort Worth region in Texas. Weightings based on a Bayesian inference technique and screenings based on model performance and statistical tests of significance are used to generate probabilistic representation of O3 response to emissions and model input parameters. Adjusted (observation-constrained) results favor simulations using the sixth version of the carbon bond chemical mechanism (CB6) and scaled-up emissions of NOx, dampening the overall sensitivity of O3 to NOx and increasing the sensitivity of O3 to volatile organic compounds in the study region. This approach of using observations to adjust and constrain model simulations can provide probabilistic representations of pollutant responsiveness to emission controls that complement the results obtained from deterministic air-quality modeling.