विधियों की तुलना करें

	मार्कोव चेन मोंटे कार्लो (MCMC)	रिज रिग्रेशन
क्षेत्र	बायेसियन	मशीन अधिगम
परिवार	Bayesian methods	Machine learning
उद्भव वर्ष	—	1970
प्रवर्तक	—	Hoerl, A.E. & Kennard, R.W.
प्रकार	Posterior sampling algorithm	L2-regularized linear regression
मौलिक स्रोत	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	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
उपनाम	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
संबंधित	3	4
सारांश	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.