পদ্ধতির তুলনা করুন

	এম্পিরিক্যাল বেয়েস	বেয়েশীয় রিগ্রেশন	মার্কভ চেইন মন্টি কার্লো (MCMC)	রিজ রিগ্রেশন
ক্ষেত্র	বেইসীয়	বেইসীয়	বেইসীয়	যন্ত্র শিখন
পরিবার	Bayesian methods	Bayesian methods	Bayesian methods	Machine learning
উদ্ভবের বছর	—	—	—	1970
প্রবর্তক	Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)	—	—	Hoerl, A.E. & Kennard, R.W.
ধরন	Empirical Bayes estimator	Bayesian linear model	Posterior sampling algorithm	L2-regularized linear regression
মৌলিক উৎস	Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press.	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	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.
অপর নাম	EB, empirical Bayes estimation, marginal likelihood estimation, James-Stein shrinkage	bayesian linear regression, probabilistic regression, bayesian regresyon	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
সম্পর্কিত	4	2	3	4
সারসংক্ষেপ	Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian inference.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.