Compară metode

	Estimarea prin Verosimilitate Maximă	Algoritmul EM
Domeniu	Statistică	Statistică
Familie	Regression model	Machine learning
Anul apariției	1922	1977
Autorul original	R. A. Fisher	Dempster, Laird & Rubin
Tip	Parametric point estimator	Iterative optimization algorithm
Sursa seminală	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.	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.
Denumiri alternative	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood	EM, Expectation-Maximization, Maximum Likelihood via Incomplete Data, BM Algoritması
Înrudite	4	2
Rezumat	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.	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.