Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms

We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation–maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic...

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Veröffentlicht in:	Statistics and computing Jg. 34; H. 2
Hauptverfasser:	Brusa, Luca, Pennoni, Fulvia, Bartolucci, Francesco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Science Evolutionary algorithms Mathematical models Maxima Maximum likelihood estimation Monte Carlo simulation Original Paper Parameter estimation Probability and Statistics in Computer Science Statistical Theory and Methods Statistics and Computing/Statistics Programs Maximum-likelihood estimation Expectation–maximization algorithm Variational expectation–maximization algorithm Local maxima
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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