  Modern cosmological data demand modern data analysis techniques. We introduce
BayOp, a new likelihood sampling and maximisation method which is based on the
Bayesian Optimisation algorithm and learns a function instead of randomly
sampling from it. We apply BayOp to analyse Planck data for traces of
inflationary features models with global periodic modulations of the primordial
power spectrum. While we do not find any new evidence for features, we
demonstrate that BayOp provides an extremely efficient way of sampling
likelihoods over low-to-moderate-dimensional parameter spaces, even for very
complex likelihood landscapes.
