This work represents a collaborative aerodynamic data collection and modeling/simulation with validation/uncertainty quantification (VUQ) for hypersonic applications. Data are collected in Lawrence Livermore National Laboratory’s Energy Matter Integration Tunnel (EMIT) for two geometries; a 10 degree half-angle conical vehicle and a 7 degree half-angle conical in a cone-slice-flag configuration. CFD simulations of the two are performed using three fidelities of commonly used CFD models. Validation with uncertainty quantification of the modeling efforts is demonstrated using a Bayesian paradigm. Two specific Bayesian models are investigated and compared, which account for scenario, model parametric, and latent errors with both models quantifying discrepancy between the simulated and measured force and pitch coefficients. Initial results demonstrate posterior learning across quantities of interest given a set of informed and uninformed priors. Based on initial results, a coefficient scaling model is proposed as a function of angle of attack that includes learned parameters from the Bayesian inference. Applied to the low fidelity model results, the posterior predictions of the coefficients are improved. Further studies will include investigation of similar models to test predictivity on current and new geometries.