Regularization and decimation pseudolikelihood approaches to statistical inference inXYspin models

We implement a pseudolikelihood approach with l1 and l2 regularizations as well as the recently introduced pseudolikelihood with decimation procedure to the inverse problem in continuous spin models on arbitrary networks, with arbitrarily disordered couplings. Performances of the approaches are tested against data produced by Monte Carlo numerical simulations and compared also to previously studied fully connected mean-field-based inference techniques. The results clearly show that the best network reconstruction is obtained through the decimation scheme, which also allows us to make the inference down to lower temperature regimes. Possible applications to phasor models for light propagation in random media are proposed and discussed.