  In regression problems where there is no known true underlying model,
conformal prediction methods enable prediction intervals to be constructed
without any assumptions on the distribution of the underlying data, except that
the training and test data are assumed to be exchangeable. However, these
methods bear a heavy computational cost-and, to be carried out exactly, the
regression algorithm would need to be fitted infinitely many times. In
practice, the conformal prediction method is run by simply considering only a
finite grid of finely spaced values for the response variable. This paper
develops discretized conformal prediction algorithms that are guaranteed to
cover the target value with the desired probability, and that offer a tradeoff
between computational cost and prediction accuracy.
