Deterministic learning for maximum-likelihood estimation through neural networks.
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.
Cervellera
Cristiano
C
Istituto di Studi sui Sistemi Intelligentiper l'Automazione, Consiglio Nazionale delle Ricerche, Genova 16149, Italy. cervellera@ge.issia.cnr.it
Macciò
Danilo
D
Muselli
Marco
M
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016013:Likelihood Functions; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*