  This paper develops a new global optimisation method that applies to a family
of criteria that are not entirely known. This family includes the criteria
obtained from the class of posteriors that have nor-malising constants that are
analytically not tractable. The procedure applies to posterior probability
densities that are continuously differen-tiable with respect to their
parameters. The proposed approach avoids the re-sampling needed for the
classical Monte Carlo maximum likelihood inference, while providing the missing
convergence properties of the ABC based methods. Results on simulated data and
real data are presented. The real data application fits an inhomogeneous area
interaction point process to cosmological data. The obtained results validate
two important aspects of the galaxies distribution in our Universe : proximity
of the galaxies from the cosmic filament network together with territorial
clustering at given range of interactions. Finally, conclusions and
perspectives are depicted.
