Calibrating full-field crystal plasticity (CP) models using global stress-strain curves alone often produces non-unique parametrizations: multiple parameter sets can predict the same global behavior but different local, grain-scale behavior. Incorporating local calibration data can mitigate uniqueness issues, but this typically requires specialized experiments like high-energy X-ray diffraction microscopy. The computational expense of full-field simulations also prevents uncertainty quantification with sampling-based probabilistic calibration algorithms like Markov chain Monte Carlo. In this study, a two-stage probabilistic calibration procedure is developed that uses both global and local data and balances the efficiency of a surrogate model with the accuracy of a full-field CP model. To enable tractable Bayesian inference via sampling from the full-field model, an efficient, parallelized sequential Monte Carlo (SMC) algorithm is used to perform the calibration, which involves more than 100,000 CP simulations. Synthetic measurement data is used in the calibration to assess uncertainty and accuracy of the parameter posterior distributions relative to a "ground-truth" simulation with a microstructure representative of Inconel 718. The two-stage calibration is shown to be robust to limited and noisy local data. Posterior distributions 'and computational costs are compared between the two-stage calibration and a one-stage calibration using only the full-field model, which is also enabled by SMC, as well as an efficient but biased one-stage calibration using a surrogate model. Overall, including local measurement data is shown to reduce parameter uncertainty, while joint distributions of the calibrated parameters highlight important considerations in choosing constitutive models and calibration data, including challenges resulting from correlated parameters.

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