  Based on a 20-year-long multiband observation of its light-curve, it was
conjectured that the quasar SDSSJ025214.67-002813.7 has a periodicity of ~4.4
years. These observations were acquired at a highly irregular sampling rate and
feature long intervals of missing data. In this setting, the inference over the
light-curve's spectral content requires, in addition to classic Fourier
methods, a proper model of the probability distribution of the missing
observations. In this article, we address the detection of the periodicity of a
light-curve from partial and irregularly-sampled observations using Gaussian
processes, a Bayesian nonparametric model for time series. This methodology
allows us to evaluate the veracity of the claimed periodicity of the
abovementioned quasar and also to estimate its power spectral density. Our main
contribution is the confirmation that considering periodic component definitely
improves the modeling of the data, although being the source originally
selected by a large sample of objects, the possibility that this is a chance
result cannot be ruled out.
