Compară metode

	Regresia Logistică	Metoda Monte Carlo cu Lanțuri Markov (MCMC)
Domeniu	Statistică pentru cercetare	Bayesian
Familie	Process / pipeline	Bayesian methods
Anul apariției	1958	—
Autorul original	David Roxbee Cox	—
Tip	Method	Posterior sampling algorithm
Sursa seminală	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Denumiri alternative	logit model, binomial logistic regression, LR	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Înrudite	3	3
Rezumat	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.