Porównaj metody

	Bayesowskie uogólnione modele addytywne (Bayesian GAM)	Model bayesowski z efektami mieszanymi
Dziedzina	Statystyka	Statystyka
Rodzina	Regression model	Regression model
Rok powstania	1990s–2000s	1990s–2000s (modern Bayesian MCMC era)
Twórca	Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others	Gelman, Hill, and the broader Bayesian hierarchical modeling tradition
Typ	Semiparametric Bayesian regression	Bayesian regression model
Źródło pierwotne	Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. ISBN: 9781498728331	Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891
Inne nazwy	Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression	Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model
Pokrewne	4	5
Podsumowanie	Bayesian Generalized Additive Models extend the frequentist GAM framework by placing prior distributions over the smooth functions and any additional model parameters. This yields full posterior distributions over each smooth effect, enabling principled uncertainty quantification, automatic smoothness selection via hyperpriors, and seamless integration with hierarchical or mixed-effects structures.	The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously.