Shadow Simulated Annealing: A new algorithm for approximate Bayesian inference of Gibbs point processes
Abstract
This paper presents a new algorithm for statistical inference and analysis of spatial patterns assumed to be realisations of Gibbspoint processes. This approach has a general character and it contributes to the existing methods based on Approximate BayesianComputation, by providing control properties of the proposed solution. 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 vali-date two important aspects of the galaxies distribution in our Universe: proximity of the galaxies from the cosmic filamentnetwork together with territorial clustering at given range of interactions. Finally, conclusions and perspectives are depicted.
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