  Gaussian Processes and the Kullback-Leibler divergence have been deeply
studied in Statistics and Machine Learning. This paper marries these two
concepts and introduce the local Kullback-Leibler divergence to learn about
intervals where two Gaussian Processes differ the most. We address subtleties
entailed in the estimation of local divergences and the corresponding interval
of local maximum divergence as well. The estimation performance and the
numerical efficiency of the proposed method are showcased via a Monte Carlo
simulation study. In a medical research context, we assess the potential of the
devised tools in the analysis of electrocardiogram signals.
