Sammenlign metoder

	Bayesiansk Generaliseret Lineær Model	Bayesiansk Negativ Binomial Regression
Fagområde	Statistik	Statistik
Familie	Regression model	Regression model
Oprindelsesår	1989 (GLM); 1995 (Bayesian BDA)	1990s–2000s
Ophavsperson	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi
Type	Bayesian regression model	Bayesian GLM for overdispersed counts
Oprindelig kilde	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
Aliasser	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model
Relaterede	6	6
Resumé	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.	Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates.