विधियों की तुलना करें

	लॉजिस्टिक रिग्रेशन	मार्कोव चेन मोंटे कार्लो (MCMC)
क्षेत्र	अनुसंधान सांख्यिकी	बायेसियन
परिवार	Process / pipeline	Bayesian methods
उद्भव वर्ष	1958	—
प्रवर्तक	David Roxbee Cox	—
प्रकार	Method	Posterior sampling algorithm
मौलिक स्रोत	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
उपनाम	logit model, binomial logistic regression, LR	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
संबंधित	3	3
सारांश	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.