Abstract: In this paper, Bayesian statistics is applied for the uncertainty evaluation of accelerometer calibration results. The process of analyzing measurement uncertainty based on GUM, GUM S1, and Bayesian statistics for linear measurement models is first presented to illustrate the differences in the analysis of the three methods. Combined with the vibration and shock calibration accelerometer data in actual work, the Bayesian statistics and GUM series methods with different prior distributions were used to analyze and compare the results. For the estimation of the reference value and its uncertainty for the key comparison in the field of shock acceleration, two different statistical models were developed using the Bayesian unpooled method and numerical method, on which the reference values and the corresponding uncertainties were calculated in combination with the Markov chain Monte Carlo method (MCMC) comparison, and the results were compared with those of the general method. The advantages and disadvantages of Bayesian statistics for uncertainty assessment are illustrated by the consistency and variability of the results obtained by the different methods.