Simulation of the restoration of unobserved areas. The GMM is trained with K = 9 Gaussians using different observed parts of the whole sky from Mock I and the full maps of Mock II--VI. The predictions are determined from the CPD marginalized over the respective partial maps of Mock I. Panel a: prediction of Mock I with only the disk information as GMM input. Panel b: difference between the original Mock I map and the prediction computed from only the disk information, rms = 1.64. Panel c: prediction of Mock I with the disk and only a small part of the southern hemisphere information as GMM input. Panel d: difference between the original Mock I map and the prediction computed from the disk and a small part of the southern hemisphere background information, rms = 0.14. Panel e: prediction of Mock I with only the southern hemisphere information as GMM input. Panel f: difference between the original Mock I map and the prediction computed from the southern hemisphere information, rms = 0.07.