Compară metode

	Algoritmul EM	Estimarea prin Verosimilitate Maximă
Domeniu	Statistică	Statistică
Familie	Machine learning	Regression model
Anul apariției	1977	1922
Autorul original	Dempster, Laird & Rubin	R. A. Fisher
Tip	Iterative optimization algorithm	Parametric point estimator
Sursa seminală	Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.
Denumiri alternative	EM, Expectation-Maximization, Maximum Likelihood via Incomplete Data, BM Algoritması	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood
Înrudite	2	4
Rezumat	The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.	Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.