  This study aims to investigate the utilization of Bayesian techniques for the
calibration of micro-electro-mechanical systems (MEMS) accelerometers. These
devices have garnered substantial interest in various practical applications
and typically require calibration through error-correcting functions. The
parameters of these error-correcting functions are determined during a
calibration process. However, due to various sources of noise, these parameters
cannot be determined with precision, making it desirable to incorporate
uncertainty in the calibration models. Bayesian modeling offers a natural and
complete way of reflecting uncertainty by treating the model parameters as
variables rather than fixed values. Additionally, Bayesian modeling enables the
incorporation of prior knowledge, making it an ideal choice for calibration.
Nevertheless, it is infrequently used in sensor calibration. This study
introduces Bayesian methods for the calibration of MEMS accelerometer data in a
straightforward manner using recent advances in probabilistic programming.
