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We develop a model for characterizing amplitude and phase probability distributions of eddy-current signals and propose a maximum likelihood (ML) method for estimating the amplitude and phase distribution parameters from measurements corrupted by additive complex white Gaussian noise. The squared amplitudes and phases of the potential defect signals are modeled as independent, identically distributed (i.i.d.) random variables following gamma and von Mises distributions, respectively. Newton-Raphson iteration is utilized to compute the ML estimates of the unknown parameters. We also compute Crame´r-Rao bounds (CRBs) for the unknown parameters and discuss initialization of the Newton-Raphson iteration. The proposed method is applied to analyze rotating-probe eddy-current data from steam-generator tube inspection in nuclear power plants. The obtained estimates can be utilized for maximum a posteriori (MAP) signal phase and amplitude estimation, as well as efficient feature extractors in a defect classification scheme. We present numerical examples with both real and simulated data to demonstrate the performance of the proposed methods.