Ground-motion prediction equations are commonly developed using regression techniques that treat the input variables or parameters as exact, neglecting the uncertainty associated with the measurement of shear-wave velocity, moment magnitude, and site-to-source distance. This parameter uncertainty propagates through the regression procedure and results in a model uncertainty that overestimates the inherent variability of the ground motion. We present methods of quantifying the uncertainty of the input parameters and incorporating the parameter uncertainty into the regression of ground-motion data using a Bayesian framework. The quantified parameter uncertainty of VS30 is explicitly accounted for in this study, and a reduction in the sigma (standard deviation of ground-motion prediction equation in natural log units) of on average 5% and upwards of 10% in the longer period spectral ordinates is realized.

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