Vertaile menetelmiä

	Markov-ketju-Monte Carlo (MCMC)	Mixed Effects Model	Harjanneregressio
Tieteenala	Bayesilainen tilastotiede	Tilastotiede	Koneoppiminen
Menetelmäperhe	Bayesian methods	Regression model	Machine learning
Syntyvuosi	—	1982	1970
Kehittäjä	—	Laird & Ware	Hoerl, A.E. & Kennard, R.W.
Tyyppi	Posterior sampling algorithm	Mixed effects regression	L2-regularized linear regression
Alkuperäislähde	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	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Rinnakkaisnimet	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	LME, LMM, mixed model, random effects model	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liittyvät	3	4	4
Tiivistelmä	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.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.	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.