方法对比

	马尔可夫链蒙特卡洛 (MCMC)	岭回归（Ridge Regression）
领域	贝叶斯	机器学习
方法族	Bayesian methods	Machine learning
起源年份	—	1970
提出者	—	Hoerl, A.E. & Kennard, R.W.
类型	Posterior sampling algorithm	L2-regularized linear regression
开创性文献	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.
别名	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
相关	3	4
摘要	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.