  This paper considers the problem of estimating linear dynamic system models
when the observations are corrupted by random disturbances with nonstandard
distributions. The paper is particularly motivated by applications where sensor
imperfections involve significant contribution of outliers or wrap-around
issues resulting in multi-modal distributions such as commonly encountered in
robotics applications. As will be illustrated, these nonstandard measurement
errors can dramatically compromise the effectiveness of standard estimation
methods, while a computational Bayesian approach developed here is demonstrated
to be equally effective as standard methods in standard measurement noise
scenarios, but dramatically more effective in nonstandard measurement noise
distribution scenarios.
