  A method to perform unfolding with Gaussian processes (GPs) is presented.
Using Bayesian regression, we define an estimator for the underlying truth
distribution as the mode of the posterior. We show that in the case where the
bin contents are distributed approximately according to a Gaussian, this
estimator is equivalent to the mean function of a GP conditioned on the maximum
likelihood estimator. Regularisation is introduced via the kernel function of
the GP, which has a natural interpretation as the covariance of the underlying
distribution. This novel approach allows for the regularisation to be informed
by prior knowledge of the underlying distribution, and for it to be varied
along the spectrum. In addition, the full statistical covariance matrix for the
estimator is obtained as part of the result. The method is applied to two
examples: a double-peaked bimodal distribution and a falling spectrum.
