Efficient global maximum likelihood estimation through kernel methods

A new efficient technique for estimating probability densities from data through the application of the
approximate global maximum likelihood (AGML) approach is proposed. It employs a composition of
kernel functions to estimate the correct behavior of parameters involved in the expression of the unknown
probability density. Convergence to the optimal solution is guaranteed by a deterministic learning
framework when low discrepancy sequences are used to generate the centers of the kernels. Trials on
mixture of Gaussians show that the proposed semi-local technique is able to efficiently approximate
the maximum likelihood solution even in complex situations where implementations based on standard
neural networks require an excessive computational cost.