Sammenlign metoder

	Bayesiansk robust regresjon	Bayesiansk generalisert lineært modell
Fagfelt	Statistikk	Statistikk
Familie	Regression model	Regression model
Opprinnelsesår	1993	1989 (GLM); 1995 (Bayesian BDA)
Opphavsperson	Geweke (1993); Gelman et al. (2013)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Type	Bayesian regression with heavy-tailed errors	Bayesian regression model
Opprinnelig kilde	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	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
Alias	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Relaterte	6	6
Sammendrag	Bayesian Robust Regression replaces the Gaussian error assumption of ordinary linear regression with a heavy-tailed distribution — most commonly the Student-t — and estimates all parameters in a Bayesian framework. The heavier tails give outliers less influence on the fitted line, yielding stable coefficient estimates and honest uncertainty intervals even when the data contain unusual observations.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.