Complex self-motion stimulations in the dark can be powerfully disorienting and can create illusory motion percepts. In the absence of visual cues, the brain has to use angular and linear acceleration information provided by the vestibular canals and the otoliths, respectively. However, these sensors are inaccurate and ambiguous. We propose that the brain processes these signals in a statistically optimal fashion, reproducing the rules of Bayesian inference. We also suggest that this processing is related to the statistics of natural head movements. This would create a perceptual bias in favour of low velocity and acceleration. We have constructed a Bayesian model of self-motion perception based on these assumptions. Using this model, we have simulated perceptual responses to centrifugation and off-vertical axis rotation and obtained close agreement with experimental findings. This demonstrates how Bayesian inference allows to make a quantitative link between sensor noise and ambiguities, statistics of head movement, and the perception of self-motion.