Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TNN.2008.2000577 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
likelihood function,neural networks,close approximation,general method,unknown density,learning (artificial intelligence),deterministic learning (dl),maximum likelihood estimation,variation,ml estimator functions,deterministic learning,discrepancy,maximum-likelihood estimation,estimator function,maximum-likelihood estimation (mle),true ml estimator function,unknown parameter,neural nets,corresponding exact solution,deterministic generation,learning artificial intelligence,maximum likelihood estimate,statistical distributions,exact solution,random variables,telecommunications,density functional theory,neural network,support vector machines,parameter estimation | Random variable,Likelihood function,Probability distribution,Rate of convergence,Artificial intelligence,Estimation theory,Artificial neural network,Deterministic system (philosophy),Machine learning,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
19 | 8 | 1941-0093 |
Citations | PageRank | References |
6 | 0.62 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristiano Cervellera | 1 | 226 | 23.63 |
| Danilo Macciò | 2 | 64 | 10.95 |
| Marco Muselli | 3 | 6 | 0.62 |