  Cerebral autoregulation refers to the brain's ability to maintain cerebral
blood flow at an approximately constant level, despite changes in arterial
blood pressure. The performance of this mechanism is often assessed using a
ten-scale index called the ARI (autoregulation index). Here, $0$ denotes the
absence of, while $9$ denotes the strongest, autoregulation. Current methods to
calculate the ARI do not typically provide error estimates. Here, we show how
this can be done using a bayesian approach. We use Markov-chain Monte Carlo
methods to produce a probability distribution for the ARI, which gives a
natural way to estimate error.
