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This paper develops a new global optimisation method that applies to a familyof criteria that are not entirely known. This family includes the criteriaobtained from the class of posteriors that have nor-malising constants that areanalytically not tractable. The procedure applies to posterior probabilitydensities that are continuously differen-tiable with respect to theirparameters. The proposed approach avoids the re-sampling needed for theclassical Monte Carlo maximum likelihood inference, while providing the missingconvergence properties of the ABC based methods. Results on simulated data andreal data are presented. The real data application fits an inhomogeneous areainteraction point process to cosmological data. The obtained results validatetwo important aspects of the galaxies distribution in our Universe : proximityof the galaxies from the cosmic filament network together with territorialclustering at given range of interactions. Finally, conclusions andperspectives are depicted.

Shadow Simulated Annealing algorithm: a new tool for global optimisation and statistical inference