PUMA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Cervellera C., Macciò D., Muselli M. Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks. In: Ieee Transactions on Neural Networks, vol. 19 (8) pp. 1456 - 1467. IEEE, 2008.
 
 
Abstract
(English)
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find close approximations to ML estimator functions for the unknown parameters of any given density. The method relies on the choice of a proper neural network and on the deterministic generation of samples of observations of the likelihood function, thus avoiding the problem of generating samples with the unknown density. Under mild assumptions, consistency and convergence with favorable rates to the true ML estimator function can be proved. Simulation results are provided to show the good behavior of the algorithm compared to the corresponding exact solutions.
DOI: 10.1109/TNN.2008.2000577
Subject deterministic learning (DL)
discrepancy
maximum-likelihood estimation (MLE)
variation


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